Statistical modeling for degradation data

CERN Document Server

Lio, Yuhlong; Ng, Hon; Tsai, Tzong-Ru

2017-01-01

This book focuses on the statistical aspects of the analysis of degradation data. In recent years, degradation data analysis has come to play an increasingly important role in different disciplines such as reliability, public health sciences, and finance. For example, information on products’ reliability can be obtained by analyzing degradation data. In addition, statistical modeling and inference techniques have been developed on the basis of different degradation measures. The book brings together experts engaged in statistical modeling and inference, presenting and discussing important recent advances in degradation data analysis and related applications. The topics covered are timely and have considerable potential to impact both statistics and reliability engineering.

Exclusion statistics and integrable models

International Nuclear Information System (INIS)

Mashkevich, S.

1998-01-01

The definition of exclusion statistics, as given by Haldane, allows for a statistical interaction between distinguishable particles (multi-species statistics). The thermodynamic quantities for such statistics ca be evaluated exactly. The explicit expressions for the cluster coefficients are presented. Furthermore, single-species exclusion statistics is realized in one-dimensional integrable models. The interesting questions of generalizing this correspondence onto the higher-dimensional and the multi-species cases remain essentially open

Statistical Models of Adaptive Immune populations

Science.gov (United States)

Sethna, Zachary; Callan, Curtis; Walczak, Aleksandra; Mora, Thierry

The availability of large (104-106 sequences) datasets of B or T cell populations from a single individual allows reliable fitting of complex statistical models for naïve generation, somatic selection, and hypermutation. It is crucial to utilize a probabilistic/informational approach when modeling these populations. The inferred probability distributions allow for population characterization, calculation of probability distributions of various hidden variables (e.g. number of insertions), as well as statistical properties of the distribution itself (e.g. entropy). In particular, the differences between the T cell populations of embryonic and mature mice will be examined as a case study. Comparing these populations, as well as proposed mixed populations, provides a concrete exercise in model creation, comparison, choice, and validation.

Growth Curve Models and Applications : Indian Statistical Institute

CERN Document Server

2017-01-01

Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas.   There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of app lied work, and these contributions have been externally r...

Online Statistical Modeling (Regression Analysis) for Independent Responses

Science.gov (United States)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

Probing NWP model deficiencies by statistical postprocessing

DEFF Research Database (Denmark)

Rosgaard, Martin Haubjerg; Nielsen, Henrik Aalborg; Nielsen, Torben S.

2016-01-01

The objective in this article is twofold. On one hand, a Model Output Statistics (MOS) framework for improved wind speed forecast accuracy is described and evaluated. On the other hand, the approach explored identifies unintuitive explanatory value from a diagnostic variable in an operational....... Based on the statistical model candidates inferred from the data, the lifted index NWP model diagnostic is consistently found among the NWP model predictors of the best performing statistical models across sites....

Statistical Model of Extreme Shear

DEFF Research Database (Denmark)

Hansen, Kurt Schaldemose; Larsen, Gunner Chr.

2005-01-01

In order to continue cost-optimisation of modern large wind turbines, it is important to continuously increase the knowledge of wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describes the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of full-scale measurements recorded with a high sampling rate...

Statistical Compression for Climate Model Output

Science.gov (United States)

Hammerling, D.; Guinness, J.; Soh, Y. J.

2017-12-01

Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus is it important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. We decompress the data by computing conditional expectations and conditional simulations from the model given the summary statistics. Conditional expectations represent our best estimate of the original data but are subject to oversmoothing in space and time. Conditional simulations introduce realistic small-scale noise so that the decompressed fields are neither too smooth nor too rough compared with the original data. Considerable attention is paid to accurately modeling the original dataset-one year of daily mean temperature data-particularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers.

Statistical models and NMR analysis of polymer microstructure

Science.gov (United States)

Statistical models can be used in conjunction with NMR spectroscopy to study polymer microstructure and polymerization mechanisms. Thus, Bernoullian, Markovian, and enantiomorphic-site models are well known. Many additional models have been formulated over the years for additional situations. Typica...

Automated statistical modeling of analytical measurement systems

International Nuclear Information System (INIS)

Jacobson, J.J.

1992-01-01

The statistical modeling of analytical measurement systems at the Idaho Chemical Processing Plant (ICPP) has been completely automated through computer software. The statistical modeling of analytical measurement systems is one part of a complete quality control program used by the Remote Analytical Laboratory (RAL) at the ICPP. The quality control program is an integration of automated data input, measurement system calibration, database management, and statistical process control. The quality control program and statistical modeling program meet the guidelines set forth by the American Society for Testing Materials and American National Standards Institute. A statistical model is a set of mathematical equations describing any systematic bias inherent in a measurement system and the precision of a measurement system. A statistical model is developed from data generated from the analysis of control standards. Control standards are samples which are made up at precise known levels by an independent laboratory and submitted to the RAL. The RAL analysts who process control standards do not know the values of those control standards. The object behind statistical modeling is to describe real process samples in terms of their bias and precision and, to verify that a measurement system is operating satisfactorily. The processing of control standards gives us this ability

Statistical modelling for ship propulsion efficiency

DEFF Research Database (Denmark)

Petersen, Jóan Petur; Jacobsen, Daniel J.; Winther, Ole

2012-01-01

This paper presents a state-of-the-art systems approach to statistical modelling of fuel efficiency in ship propulsion, and also a novel and publicly available data set of high quality sensory data. Two statistical model approaches are investigated and compared: artificial neural networks...

Effects of statistical models and items difficulties on making trait-level inferences: A simulation study

Directory of Open Access Journals (Sweden)

Nelson Hauck Filho

2014-12-01

Full Text Available Researchers dealing with the task of estimating locations of individuals on continuous latent variables may rely on several statistical models described in the literature. However, weighting costs and benefits of using one specific model over alternative models depends on empirical information that is not always clearly available. Therefore, the aim of this simulation study was to compare the performance of seven popular statistical models in providing adequate latent trait estimates in conditions of items difficulties targeted at the sample mean or at the tails of the latent trait distribution. Results suggested an overall tendency of models to provide more accurate estimates of true latent scores when using items targeted at the sample mean of the latent trait distribution. Rating Scale Model, Graded Response Model, and Weighted Least Squares Mean- and Variance-adjusted Confirmatory Factor Analysis yielded the most reliable latent trait estimates, even when applied to inadequate items for the sample distribution of the latent variable. These findings have important implications concerning some popular methodological practices in Psychology and related areas.

Sensometrics: Thurstonian and Statistical Models

DEFF Research Database (Denmark)

Christensen, Rune Haubo Bojesen

. sensR is a package for sensory discrimination testing with Thurstonian models and ordinal supports analysis of ordinal data with cumulative link (mixed) models. While sensR is closely connected to the sensometrics field, the ordinal package has developed into a generic statistical package applicable......This thesis is concerned with the development and bridging of Thurstonian and statistical models for sensory discrimination testing as applied in the scientific discipline of sensometrics. In sensory discrimination testing sensory differences between products are detected and quantified by the use...... and sensory discrimination testing in particular in a series of papers by advancing Thurstonian models for a range of sensory discrimination protocols in addition to facilitating their application by providing software for fitting these models. The main focus is on identifying Thurstonian models...

A Statistical Model for Regional Tornado Climate Studies.

Science.gov (United States)

Jagger, Thomas H; Elsner, James B; Widen, Holly M

2015-01-01

Tornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA). A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.

Statistical modelling for social researchers principles and practice

CERN Document Server

Tarling, Roger

2008-01-01

This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling. Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research question to be addressed. To facilitate application, the book also offers practical guidance and instruction in fitting models using SPSS and Stata, the most popular statistical computer software which is available to most social researchers. Instruction in using MLwiN is also given. Models covered in the book include; multiple regression, binary, multinomial and ordered logistic regression, log-l...

Statistical mechanics of directed models of polymers in the square lattice

CERN Document Server

Rensburg, J V

2003-01-01

Directed square lattice models of polymers and vesicles have received considerable attention in the recent mathematical and physical sciences literature. These are idealized geometric directed lattice models introduced to study phase behaviour in polymers, and include Dyck paths, partially directed paths, directed trees and directed vesicles models. Directed models are closely related to models studied in the combinatorics literature (and are often exactly solvable). They are also simplified versions of a number of statistical mechanics models, including the self-avoiding walk, lattice animals and lattice vesicles. The exchange of approaches and ideas between statistical mechanics and combinatorics have considerably advanced the description and understanding of directed lattice models, and this will be explored in this review. The combinatorial nature of directed lattice path models makes a study using generating function approaches most natural. In contrast, the statistical mechanics approach would introduce...

Shell model in large spaces and statistical spectroscopy

International Nuclear Information System (INIS)

Kota, V.K.B.

1996-01-01

For many nuclear structure problems of current interest it is essential to deal with shell model in large spaces. For this, three different approaches are now in use and two of them are: (i) the conventional shell model diagonalization approach but taking into account new advances in computer technology; (ii) the shell model Monte Carlo method. A brief overview of these two methods is given. Large space shell model studies raise fundamental questions regarding the information content of the shell model spectrum of complex nuclei. This led to the third approach- the statistical spectroscopy methods. The principles of statistical spectroscopy have their basis in nuclear quantum chaos and they are described (which are substantiated by large scale shell model calculations) in some detail. (author)

Topology for statistical modeling of petascale data.

Energy Technology Data Exchange (ETDEWEB)

Pascucci, Valerio (University of Utah, Salt Lake City, UT); Mascarenhas, Ajith Arthur; Rusek, Korben (Texas A& M University, College Station, TX); Bennett, Janine Camille; Levine, Joshua (University of Utah, Salt Lake City, UT); Pebay, Philippe Pierre; Gyulassy, Attila (University of Utah, Salt Lake City, UT); Thompson, David C.; Rojas, Joseph Maurice (Texas A& M University, College Station, TX)

2011-07-01

This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled 'Topology for Statistical Modeling of Petascale Data', funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program. Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is thus to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, our approach is based on the complementary techniques of combinatorial topology and statistical modeling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modeling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. This document summarizes the technical advances we have made to date that were made possible in whole or in part by MAPD funding. These technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modeling, and (3) new integrated topological and statistical methods.

Bayesian models: A statistical primer for ecologists

Science.gov (United States)

Hobbs, N. Thompson; Hooten, Mevin B.

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

Study on the Orion spiral arm structure by the statistical modelling method

International Nuclear Information System (INIS)

Basharina, T.S.; Pavlovskaya, E.D.; Filippova, A.A.

1980-01-01

A method of investigation of the spiral structure based on the statistical modelling methods is suggested. This method is used for the study of the Orion spiral arm. The maxima of density and the widths of the Orion arm in the direction of the areas considered for the longitude interval 55 deg - 187 deg are defined under the assumption of normal distribution of stars across the arm. The Sun is shown to be at the inner edge of the arm [ru

Analyzing sickness absence with statistical models for survival data

DEFF Research Database (Denmark)

Christensen, Karl Bang; Andersen, Per Kragh; Smith-Hansen, Lars

2007-01-01

OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness...... absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study...... involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation...

Statistical Model-Based Face Pose Estimation

Institute of Scientific and Technical Information of China (English)

GE Xinliang; YANG Jie; LI Feng; WANG Huahua

2007-01-01

A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.

Right-sizing statistical models for longitudinal data.

Science.gov (United States)

Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M

2015-12-01

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).

Mixed deterministic statistical modelling of regional ozone air pollution

KAUST Repository

Kalenderski, Stoitchko

2011-03-17

We develop a physically motivated statistical model for regional ozone air pollution by separating the ground-level pollutant concentration field into three components, namely: transport, local production and large-scale mean trend mostly dominated by emission rates. The model is novel in the field of environmental spatial statistics in that it is a combined deterministic-statistical model, which gives a new perspective to the modelling of air pollution. The model is presented in a Bayesian hierarchical formalism, and explicitly accounts for advection of pollutants, using the advection equation. We apply the model to a specific case of regional ozone pollution-the Lower Fraser valley of British Columbia, Canada. As a predictive tool, we demonstrate that the model vastly outperforms existing, simpler modelling approaches. Our study highlights the importance of simultaneously considering different aspects of an air pollution problem as well as taking into account the physical bases that govern the processes of interest. © 2011 John Wiley & Sons, Ltd..

Computationally efficient statistical differential equation modeling using homogenization

Science.gov (United States)

Hooten, Mevin B.; Garlick, Martha J.; Powell, James A.

2013-01-01

Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA.

Adaptive Maneuvering Frequency Method of Current Statistical Model

Institute of Scientific and Technical Information of China (English)

Wei Sun; Yongjian Yang

2017-01-01

Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.

Simple statistical model for branched aggregates

DEFF Research Database (Denmark)

Lemarchand, Claire; Hansen, Jesper Schmidt

2015-01-01

, given that it already has bonds with others. The model is applied here to asphaltene nanoaggregates observed in molecular dynamics simulations of Cooee bitumen. The variation with temperature of the probabilities deduced from this model is discussed in terms of statistical mechanics arguments....... The relevance of the statistical model in the case of asphaltene nanoaggregates is checked by comparing the predicted value of the probability for one molecule to have exactly i bonds with the same probability directly measured in the molecular dynamics simulations. The agreement is satisfactory......We propose a statistical model that can reproduce the size distribution of any branched aggregate, including amylopectin, dendrimers, molecular clusters of monoalcohols, and asphaltene nanoaggregates. It is based on the conditional probability for one molecule to form a new bond with a molecule...

A Statistical Model for Regional Tornado Climate Studies.

Directory of Open Access Journals (Sweden)

Thomas H Jagger

Full Text Available Tornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA. A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.

Matrix Tricks for Linear Statistical Models

CERN Document Server

Puntanen, Simo; Styan, George PH

2011-01-01

In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple "tricks" which simplify and clarify the treatment of a problem - both for the student and

Statistical Model Checking of Rich Models and Properties

DEFF Research Database (Denmark)

Poulsen, Danny Bøgsted

in undecidability issues for the traditional model checking approaches. Statistical model checking has proven itself a valuable supplement to model checking and this thesis is concerned with extending this software validation technique to stochastic hybrid systems. The thesis consists of two parts: the first part...... motivates why existing model checking technology should be supplemented by new techniques. It also contains a brief introduction to probability theory and concepts covered by the six papers making up the second part. The first two papers are concerned with developing online monitoring techniques...... systems. The fifth paper shows how stochastic hybrid automata are useful for modelling biological systems and the final paper is concerned with showing how statistical model checking is efficiently distributed. In parallel with developing the theory contained in the papers, a substantial part of this work...

Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?

Science.gov (United States)

Snell, Kym Ie; Ensor, Joie; Debray, Thomas Pa; Moons, Karel Gm; Riley, Richard D

2017-01-01

If individual participant data are available from multiple studies or clusters, then a prediction model can be externally validated multiple times. This allows the model's discrimination and calibration performance to be examined across different settings. Random-effects meta-analysis can then be used to quantify overall (average) performance and heterogeneity in performance. This typically assumes a normal distribution of 'true' performance across studies. We conducted a simulation study to examine this normality assumption for various performance measures relating to a logistic regression prediction model. We simulated data across multiple studies with varying degrees of variability in baseline risk or predictor effects and then evaluated the shape of the between-study distribution in the C-statistic, calibration slope, calibration-in-the-large, and E/O statistic, and possible transformations thereof. We found that a normal between-study distribution was usually reasonable for the calibration slope and calibration-in-the-large; however, the distributions of the C-statistic and E/O were often skewed across studies, particularly in settings with large variability in the predictor effects. Normality was vastly improved when using the logit transformation for the C-statistic and the log transformation for E/O, and therefore we recommend these scales to be used for meta-analysis. An illustrated example is given using a random-effects meta-analysis of the performance of QRISK2 across 25 general practices.

Statistical Modelling of Wind Proles - Data Analysis and Modelling

DEFF Research Database (Denmark)

Jónsson, Tryggvi; Pinson, Pierre

The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles.......The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles....

An R2 statistic for fixed effects in the linear mixed model.

Science.gov (United States)

Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

2008-12-20

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

Statistical modelling of transcript profiles of differentially regulated genes

Directory of Open Access Journals (Sweden)

Sergeant Martin J

2008-07-01

Full Text Available Abstract Background The vast quantities of gene expression profiling data produced in microarray studies, and the more precise quantitative PCR, are often not statistically analysed to their full potential. Previous studies have summarised gene expression profiles using simple descriptive statistics, basic analysis of variance (ANOVA and the clustering of genes based on simple models fitted to their expression profiles over time. We report the novel application of statistical non-linear regression modelling techniques to describe the shapes of expression profiles for the fungus Agaricus bisporus, quantified by PCR, and for E. coli and Rattus norvegicus, using microarray technology. The use of parametric non-linear regression models provides a more precise description of expression profiles, reducing the "noise" of the raw data to produce a clear "signal" given by the fitted curve, and describing each profile with a small number of biologically interpretable parameters. This approach then allows the direct comparison and clustering of the shapes of response patterns between genes and potentially enables a greater exploration and interpretation of the biological processes driving gene expression. Results Quantitative reverse transcriptase PCR-derived time-course data of genes were modelled. "Split-line" or "broken-stick" regression identified the initial time of gene up-regulation, enabling the classification of genes into those with primary and secondary responses. Five-day profiles were modelled using the biologically-oriented, critical exponential curve, y(t = A + (B + CtRt + ε. This non-linear regression approach allowed the expression patterns for different genes to be compared in terms of curve shape, time of maximal transcript level and the decline and asymptotic response levels. Three distinct regulatory patterns were identified for the five genes studied. Applying the regression modelling approach to microarray-derived time course data

Statistical shape and appearance models of bones.

Science.gov (United States)

Sarkalkan, Nazli; Weinans, Harrie; Zadpoor, Amir A

2014-03-01

When applied to bones, statistical shape models (SSM) and statistical appearance models (SAM) respectively describe the mean shape and mean density distribution of bones within a certain population as well as the main modes of variations of shape and density distribution from their mean values. The availability of this quantitative information regarding the detailed anatomy of bones provides new opportunities for diagnosis, evaluation, and treatment of skeletal diseases. The potential of SSM and SAM has been recently recognized within the bone research community. For example, these models have been applied for studying the effects of bone shape on the etiology of osteoarthritis, improving the accuracy of clinical osteoporotic fracture prediction techniques, design of orthopedic implants, and surgery planning. This paper reviews the main concepts, methods, and applications of SSM and SAM as applied to bone. Copyright © 2013 Elsevier Inc. All rights reserved.

Statistical model of a gas diffusion electrode. III. Photomicrograph study

Energy Technology Data Exchange (ETDEWEB)

Winsel, A W

1965-12-01

A linear section through a gas diffusion electrode produces a certain distribution function of sinews with the pores. From this distribution function some qualities of the pore structure are derived, and an automatic device to determine the distribution function is described. With a statistical model of a gas diffusion electrode the behavior of a DSK electrode is discussed and compared with earlier measurements of the flow resistance of this material.

Uncertainty the soul of modeling, probability & statistics

CERN Document Server

Briggs, William

2016-01-01

This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, suc...

Statistical principles for prospective study protocols:

DEFF Research Database (Denmark)

Christensen, Robin; Langberg, Henning

2012-01-01

In the design of scientific studies it is essential to decide on which scientific questions one aims to answer, just as it is important to decide on the correct statistical methods to use to answer these questions. The correct use of statistical methods is crucial in all aspects of research...... to quantify relationships in data. Despite an increased focus on statistical content and complexity of biomedical research these topics remain difficult for most researchers. Statistical methods enable researchers to condense large spreadsheets with data into means, proportions, and difference between means......, risk differences, and other quantities that convey information. One of the goals in biomedical research is to develop parsimonious models - meaning as simple as possible. This approach is valid if the subsequent research report (the article) is written independent of whether the results...

Applied systems ecology: models, data, and statistical methods

Energy Technology Data Exchange (ETDEWEB)

Eberhardt, L L

1976-01-01

In this report, systems ecology is largely equated to mathematical or computer simulation modelling. The need for models in ecology stems from the necessity to have an integrative device for the diversity of ecological data, much of which is observational, rather than experimental, as well as from the present lack of a theoretical structure for ecology. Different objectives in applied studies require specialized methods. The best predictive devices may be regression equations, often non-linear in form, extracted from much more detailed models. A variety of statistical aspects of modelling, including sampling, are discussed. Several aspects of population dynamics and food-chain kinetics are described, and it is suggested that the two presently separated approaches should be combined into a single theoretical framework. It is concluded that future efforts in systems ecology should emphasize actual data and statistical methods, as well as modelling.

Statistical Models for Social Networks

NARCIS (Netherlands)

Snijders, Tom A. B.; Cook, KS; Massey, DS

2011-01-01

Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

Distributions with given marginals and statistical modelling

CERN Document Server

Fortiana, Josep; Rodriguez-Lallena, José

2002-01-01

This book contains a selection of the papers presented at the meeting `Distributions with given marginals and statistical modelling', held in Barcelona (Spain), July 17-20, 2000. In 24 chapters, this book covers topics such as the theory of copulas and quasi-copulas, the theory and compatibility of distributions, models for survival distributions and other well-known distributions, time series, categorical models, definition and estimation of measures of dependence, monotonicity and stochastic ordering, shape and separability of distributions, hidden truncation models, diagonal families, orthogonal expansions, tests of independence, and goodness of fit assessment. These topics share the use and properties of distributions with given marginals, this being the fourth specialised text on this theme. The innovative aspect of the book is the inclusion of statistical aspects such as modelling, Bayesian statistics, estimation, and tests.

Experimental, statistical, and biological models of radon carcinogenesis

International Nuclear Information System (INIS)

Cross, F.T.

1991-09-01

Risk models developed for underground miners have not been consistently validated in studies of populations exposed to indoor radon. Imprecision in risk estimates results principally from differences between exposures in mines as compared to domestic environments and from uncertainties about the interaction between cigarette-smoking and exposure to radon decay products. Uncertainties in extrapolating miner data to domestic exposures can be reduced by means of a broad-based health effects research program that addresses the interrelated issues of exposure, respiratory tract dose, carcinogenesis (molecular/cellular and animal studies, plus developing biological and statistical models), and the relationship of radon to smoking and other copollutant exposures. This article reviews experimental animal data on radon carcinogenesis observed primarily in rats at Pacific Northwest Laboratory. Recent experimental and mechanistic carcinogenesis models of exposures to radon, uranium ore dust, and cigarette smoke are presented with statistical analyses of animal data. 20 refs., 1 fig

Actuarial statistics with generalized linear mixed models

NARCIS (Netherlands)

Antonio, K.; Beirlant, J.

2007-01-01

Over the last decade the use of generalized linear models (GLMs) in actuarial statistics has received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh and Nelder [McCullagh, P., Nelder, J.A., 1989. Generalized linear models. In: Monographs on Statistics

Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

Statistical models for competing risk analysis

International Nuclear Information System (INIS)

Sather, H.N.

1976-08-01

Research results on three new models for potential applications in competing risks problems. One section covers the basic statistical relationships underlying the subsequent competing risks model development. Another discusses the problem of comparing cause-specific risk structure by competing risks theory in two homogeneous populations, P1 and P2. Weibull models which allow more generality than the Berkson and Elveback models are studied for the effect of time on the hazard function. The use of concomitant information for modeling single-risk survival is extended to the multiple failure mode domain of competing risks. The model used to illustrate the use of this methodology is a life table model which has constant hazards within pre-designated intervals of the time scale. Two parametric models for bivariate dependent competing risks, which provide interesting alternatives, are proposed and examined

Structured statistical models of inductive reasoning.

Science.gov (United States)

Kemp, Charles; Tenenbaum, Joshua B

2009-01-01

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.

WE-A-201-02: Modern Statistical Modeling

Energy Technology Data Exchange (ETDEWEB)

Niemierko, A.

2016-06-15

Regulatory Commission and may be remembered for his critique of the National Academy of Sciences BEIR III report (stating that their methodology “imposes a Delphic quality to the .. risk estimates”.) This led to his appointment as a member of the BEIR V committee. Don presented refresher courses at the AAPM, ASTRO and RSNA meetings and was active in the AAPM as a member or chair of several committees. He was the principal author of AAPM Report 43, which is essentially a critique of established clinical studies prior to 1992. He was co-editor of the Proceedings of many symposia on Time, Dose and Fractionation held in Madison, Wisconsin. He received the AAPM lifetime Achievement award in 2004. Don’s second wife of 46 years, Ann, predeceased him and he is survived by daughters Hillary and Emily, son John and two grandsons. Don was a true gentleman with a unique and erudite writing style illuminated by pithy quotations. If he had a fault it was, perhaps, that he did not realize how much smarter he was than the rest of us. This presentation draws heavily on a biography and video interview in the History and Heritage section of the AAPM website. The quote is his own. Andrzej Niemierko: Statistical modeling plays an essential role in modern medicine for quantitative evaluation of the effect of treatment. This session will feature an overview of statistical modeling techniques used for analyzing the many types of research data and an exploration of recent advances in new statistical modeling methodologies. Learning Objectives: To learn basics of statistical modeling methodology. To discuss statistical models that are frequently used in radiation oncology To discuss advanced modern statistical modeling methods and applications.

WE-A-201-02: Modern Statistical Modeling

International Nuclear Information System (INIS)

Niemierko, A.

2016-01-01

Regulatory Commission and may be remembered for his critique of the National Academy of Sciences BEIR III report (stating that their methodology “imposes a Delphic quality to the .. risk estimates”.) This led to his appointment as a member of the BEIR V committee. Don presented refresher courses at the AAPM, ASTRO and RSNA meetings and was active in the AAPM as a member or chair of several committees. He was the principal author of AAPM Report 43, which is essentially a critique of established clinical studies prior to 1992. He was co-editor of the Proceedings of many symposia on Time, Dose and Fractionation held in Madison, Wisconsin. He received the AAPM lifetime Achievement award in 2004. Don’s second wife of 46 years, Ann, predeceased him and he is survived by daughters Hillary and Emily, son John and two grandsons. Don was a true gentleman with a unique and erudite writing style illuminated by pithy quotations. If he had a fault it was, perhaps, that he did not realize how much smarter he was than the rest of us. This presentation draws heavily on a biography and video interview in the History and Heritage section of the AAPM website. The quote is his own. Andrzej Niemierko: Statistical modeling plays an essential role in modern medicine for quantitative evaluation of the effect of treatment. This session will feature an overview of statistical modeling techniques used for analyzing the many types of research data and an exploration of recent advances in new statistical modeling methodologies. Learning Objectives: To learn basics of statistical modeling methodology. To discuss statistical models that are frequently used in radiation oncology To discuss advanced modern statistical modeling methods and applications.

Statistical modelling in biostatistics and bioinformatics selected papers

CERN Document Server

Peng, Defen

2014-01-01

This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and fu...

Statistical models for expert judgement and wear prediction

International Nuclear Information System (INIS)

Pulkkinen, U.

1994-01-01

This thesis studies the statistical analysis of expert judgements and prediction of wear. The point of view adopted is the one of information theory and Bayesian statistics. A general Bayesian framework for analyzing both the expert judgements and wear prediction is presented. Information theoretic interpretations are given for some averaging techniques used in the determination of consensus distributions. Further, information theoretic models are compared with a Bayesian model. The general Bayesian framework is then applied in analyzing expert judgements based on ordinal comparisons. In this context, the value of information lost in the ordinal comparison process is analyzed by applying decision theoretic concepts. As a generalization of the Bayesian framework, stochastic filtering models for wear prediction are formulated. These models utilize the information from condition monitoring measurements in updating the residual life distribution of mechanical components. Finally, the application of stochastic control models in optimizing operational strategies for inspected components are studied. Monte-Carlo simulation methods, such as the Gibbs sampler and the stochastic quasi-gradient method, are applied in the determination of posterior distributions and in the solution of stochastic optimization problems. (orig.) (57 refs., 7 figs., 1 tab.)

A no extensive statistical model for the nucleon structure function

International Nuclear Information System (INIS)

Trevisan, Luis A.; Mirez, Carlos

2013-01-01

We studied an application of nonextensive thermodynamics to describe the structure function of nucleon, in a model where the usual Fermi-Dirac and Bose-Einstein energy distribution were replaced by the equivalent functions of the q-statistical. The parameters of the model are given by an effective temperature T, the q parameter (from Tsallis statistics), and two chemical potentials given by the corresponding up (u) and down (d) quark normalization in the nucleon.

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

Directory of Open Access Journals (Sweden)

Shuai Luo

2016-02-01

Full Text Available Bioelectrochemical systems (BES are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

Atmospheric statistical dynamic models. Model performance: the Lawrence Livermore Laboratoy Zonal Atmospheric Model

International Nuclear Information System (INIS)

Potter, G.L.; Ellsaesser, H.W.; MacCracken, M.C.; Luther, F.M.

1978-06-01

Results from the zonal model indicate quite reasonable agreement with observation in terms of the parameters and processes that influence the radiation and energy balance calculations. The model produces zonal statistics similar to those from general circulation models, and has also been shown to produce similar responses in sensitivity studies. Further studies of model performance are planned, including: comparison with July data; comparison of temperature and moisture transport and wind fields for winter and summer months; and a tabulation of atmospheric energetics. Based on these preliminary performance studies, however, it appears that the zonal model can be used in conjunction with more complex models to help unravel the problems of understanding the processes governing present climate and climate change. As can be seen in the subsequent paper on model sensitivity studies, in addition to reduced cost of computation, the zonal model facilitates analysis of feedback mechanisms and simplifies analysis of the interactions between processes

Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

Science.gov (United States)

Andersson, C. David; Hillgren, J. Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

2015-03-01

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

A Stochastic Fractional Dynamics Model of Rainfall Statistics

Science.gov (United States)

Kundu, Prasun; Travis, James

2013-04-01

Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, that allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is designed to faithfully reflect the scale dependence and is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and times scales. The main restriction is the assumption that the statistics of the precipitation field is spatially homogeneous and isotropic and stationary in time. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and in Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to the second moment statistics of the radar data. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well without any further adjustment. Some data sets containing periods of non-stationary behavior that involves occasional anomalously correlated rain events, present a challenge for the model.

A Model of Statistics Performance Based on Achievement Goal Theory.

Science.gov (United States)

Bandalos, Deborah L.; Finney, Sara J.; Geske, Jenenne A.

2003-01-01

Tests a model of statistics performance based on achievement goal theory. Both learning and performance goals affected achievement indirectly through study strategies, self-efficacy, and test anxiety. Implications of these findings for teaching and learning statistics are discussed. (Contains 47 references, 3 tables, 3 figures, and 1 appendix.)…

Statistical Models and Methods for Lifetime Data

CERN Document Server

Lawless, Jerald F

2011-01-01

Praise for the First Edition"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."-Choice"This is an important book, which will appeal to statisticians working on survival analysis problems."-Biometrics"A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook."-Statistics in MedicineThe statistical analysis of lifetime or response time data is a key tool in engineering,

Statistical Modeling of Large Wind Plant System's Generation - A Case Study

International Nuclear Information System (INIS)

Sabolic, D.

2014-01-01

This paper presents simplistic, yet very accurate, descriptive statistical models of various static and dynamic parameters of energy output from a large system of wind plants operated by Bonneville Power Administration (BPA), USA. The system's size at the end of 2013 was 4515 MW of installed capacity. The 5-minute readings from the beginning of 2007 to the end of 2013, recorded and published by BPA, were used to derive a number of experimental distributions, which were then used to devise theoretic statistical models with merely one or two parameters. In spite of the simplicity, they reproduced experimental data with great accuracy, which was checked by rigorous tests of goodness-of-fit. Statistical distribution functions were obtained for the following wind generation-related quantities: total generation as percentage of total installed capacity; change in total generation power in 5, 10, 15, 20, 25, 30, 45, and 60 minutes as percentage of total installed capacity; duration of intervals with total generated power, expressed as percentage of total installed capacity, lower than certain pre-specified level. Limitation of total installed wind plant capacity, when it is determined by regulation demand from wind plants, is discussed, too. The models presented here can be utilized in analyses related to power system economics/policy, which is also briefly discussed in the paper. (author).

Topology for Statistical Modeling of Petascale Data

Energy Technology Data Exchange (ETDEWEB)

Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Bremer, P. -T. [Univ. of Utah, Salt Lake City, UT (United States)

2013-10-31

Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, the approach of the entire team involving all three institutions is based on the complementary techniques of combinatorial topology and statistical modelling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modelling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. The overall technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modelling, and (3) new integrated topological and statistical methods. Roughly speaking, the division of labor between our 3 groups (Sandia Labs in Livermore, Texas A&M in College Station, and U Utah in Salt Lake City) is as follows: the Sandia group focuses on statistical methods and their formulation in algebraic terms, and finds the application problems (and data sets) most relevant to this project, the Texas A&M Group develops new algebraic geometry algorithms, in particular with fewnomial theory, and the Utah group develops new algorithms in computational topology via Discrete Morse Theory. However, we hasten to point out that our three groups stay in tight contact via videconference every 2 weeks, so there is much synergy of ideas between the groups. The following of this document is focused on the contributions that had grater direct involvement from the team at the University of Utah in Salt Lake City.

Statistical models and methods for reliability and survival analysis

CERN Document Server

Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo

2013-01-01

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical

A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model

Energy Technology Data Exchange (ETDEWEB)

Pasqualini, Donatella [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2016-05-11

This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimated stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.

Model-generated air quality statistics for application in vegetation response models in Alberta

International Nuclear Information System (INIS)

McVehil, G.E.; Nosal, M.

1990-01-01

To test and apply vegetation response models in Alberta, air pollution statistics representative of various parts of the Province are required. At this time, air quality monitoring data of the requisite accuracy and time resolution are not available for most parts of Alberta. Therefore, there exists a need to develop appropriate air quality statistics. The objectives of the work reported here were to determine the applicability of model generated air quality statistics and to develop by modelling, realistic and representative time series of hourly SO 2 concentrations that could be used to generate the statistics demanded by vegetation response models

Development of a statistical model for cervical cancer cell death with irreversible electroporation in vitro.

Science.gov (United States)

Yang, Yongji; Moser, Michael A J; Zhang, Edwin; Zhang, Wenjun; Zhang, Bing

2018-01-01

The aim of this study was to develop a statistical model for cell death by irreversible electroporation (IRE) and to show that the statistic model is more accurate than the electric field threshold model in the literature using cervical cancer cells in vitro. HeLa cell line was cultured and treated with different IRE protocols in order to obtain data for modeling the statistical relationship between the cell death and pulse-setting parameters. In total, 340 in vitro experiments were performed with a commercial IRE pulse system, including a pulse generator and an electric cuvette. Trypan blue staining technique was used to evaluate cell death after 4 hours of incubation following IRE treatment. Peleg-Fermi model was used in the study to build the statistical relationship using the cell viability data obtained from the in vitro experiments. A finite element model of IRE for the electric field distribution was also built. Comparison of ablation zones between the statistical model and electric threshold model (drawn from the finite element model) was used to show the accuracy of the proposed statistical model in the description of the ablation zone and its applicability in different pulse-setting parameters. The statistical models describing the relationships between HeLa cell death and pulse length and the number of pulses, respectively, were built. The values of the curve fitting parameters were obtained using the Peleg-Fermi model for the treatment of cervical cancer with IRE. The difference in the ablation zone between the statistical model and the electric threshold model was also illustrated to show the accuracy of the proposed statistical model in the representation of ablation zone in IRE. This study concluded that: (1) the proposed statistical model accurately described the ablation zone of IRE with cervical cancer cells, and was more accurate compared with the electric field model; (2) the proposed statistical model was able to estimate the value of electric

Performance modeling, loss networks, and statistical multiplexing

CERN Document Server

Mazumdar, Ravi

2009-01-01

This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

Science.gov (United States)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

New robust statistical procedures for the polytomous logistic regression models.

Science.gov (United States)

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Tropical geometry of statistical models.

Science.gov (United States)

Pachter, Lior; Sturmfels, Bernd

2004-11-16

This article presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. Here, we address the question of how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. The Newton polytope of a statistical model plays a key role. Our results are applied to the hidden Markov model and the general Markov model on a binary tree.

12th Workshop on Stochastic Models, Statistics and Their Applications

CERN Document Server

Rafajłowicz, Ewaryst; Szajowski, Krzysztof

2015-01-01

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

Validation of statistical models for creep rupture by parametric analysis

Energy Technology Data Exchange (ETDEWEB)

Bolton, J., E-mail: john.bolton@uwclub.net [65, Fisher Ave., Rugby, Warks CV22 5HW (United Kingdom)

2012-01-15

Statistical analysis is an efficient method for the optimisation of any candidate mathematical model of creep rupture data, and for the comparative ranking of competing models. However, when a series of candidate models has been examined and the best of the series has been identified, there is no statistical criterion to determine whether a yet more accurate model might be devised. Hence there remains some uncertainty that the best of any series examined is sufficiently accurate to be considered reliable as a basis for extrapolation. This paper proposes that models should be validated primarily by parametric graphical comparison to rupture data and rupture gradient data. It proposes that no mathematical model should be considered reliable for extrapolation unless the visible divergence between model and data is so small as to leave no apparent scope for further reduction. This study is based on the data for a 12% Cr alloy steel used in BS PD6605:1998 to exemplify its recommended statistical analysis procedure. The models considered in this paper include a) a relatively simple model, b) the PD6605 recommended model and c) a more accurate model of somewhat greater complexity. - Highlights: Black-Right-Pointing-Pointer The paper discusses the validation of creep rupture models derived from statistical analysis. Black-Right-Pointing-Pointer It demonstrates that models can be satisfactorily validated by a visual-graphic comparison of models to data. Black-Right-Pointing-Pointer The method proposed utilises test data both as conventional rupture stress and as rupture stress gradient. Black-Right-Pointing-Pointer The approach is shown to be more reliable than a well-established and widely used method (BS PD6605).

Statistical modelling for recurrent events: an application to sports injuries.

Science.gov (United States)

Ullah, Shahid; Gabbett, Tim J; Finch, Caroline F

2014-09-01

Injuries are often recurrent, with subsequent injuries influenced by previous occurrences and hence correlation between events needs to be taken into account when analysing such data. This paper compares five different survival models (Cox proportional hazards (CoxPH) model and the following generalisations to recurrent event data: Andersen-Gill (A-G), frailty, Wei-Lin-Weissfeld total time (WLW-TT) marginal, Prentice-Williams-Peterson gap time (PWP-GT) conditional models) for the analysis of recurrent injury data. Empirical evaluation and comparison of different models were performed using model selection criteria and goodness-of-fit statistics. Simulation studies assessed the size and power of each model fit. The modelling approach is demonstrated through direct application to Australian National Rugby League recurrent injury data collected over the 2008 playing season. Of the 35 players analysed, 14 (40%) players had more than 1 injury and 47 contact injuries were sustained over 29 matches. The CoxPH model provided the poorest fit to the recurrent sports injury data. The fit was improved with the A-G and frailty models, compared to WLW-TT and PWP-GT models. Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving modelling of recurrent sports injury data use the frailty model in preference to the CoxPH model or its other generalisations. The paper provides a rationale for future statistical modelling approaches for recurrent sports injury. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Statistical Validation of Engineering and Scientific Models: Background

International Nuclear Information System (INIS)

Hills, Richard G.; Trucano, Timothy G.

1999-01-01

A tutorial is presented discussing the basic issues associated with propagation of uncertainty analysis and statistical validation of engineering and scientific models. The propagation of uncertainty tutorial illustrates the use of the sensitivity method and the Monte Carlo method to evaluate the uncertainty in predictions for linear and nonlinear models. Four example applications are presented; a linear model, a model for the behavior of a damped spring-mass system, a transient thermal conduction model, and a nonlinear transient convective-diffusive model based on Burger's equation. Correlated and uncorrelated model input parameters are considered. The model validation tutorial builds on the material presented in the propagation of uncertainty tutoriaI and uses the damp spring-mass system as the example application. The validation tutorial illustrates several concepts associated with the application of statistical inference to test model predictions against experimental observations. Several validation methods are presented including error band based, multivariate, sum of squares of residuals, and optimization methods. After completion of the tutorial, a survey of statistical model validation literature is presented and recommendations for future work are made

Statistical models for optimizing mineral exploration

International Nuclear Information System (INIS)

Wignall, T.K.; DeGeoffroy, J.

1987-01-01

The primary purpose of mineral exploration is to discover ore deposits. The emphasis of this volume is on the mathematical and computational aspects of optimizing mineral exploration. The seven chapters that make up the main body of the book are devoted to the description and application of various types of computerized geomathematical models. These chapters include: (1) the optimal selection of ore deposit types and regions of search, as well as prospecting selected areas, (2) designing airborne and ground field programs for the optimal coverage of prospecting areas, and (3) delineating and evaluating exploration targets within prospecting areas by means of statistical modeling. Many of these statistical programs are innovative and are designed to be useful for mineral exploration modeling. Examples of geomathematical models are applied to exploring for six main types of base and precious metal deposits, as well as other mineral resources (such as bauxite and uranium)

Security of statistical data bases: invasion of privacy through attribute correlational modeling

Energy Technology Data Exchange (ETDEWEB)

Palley, M.A.

1985-01-01

This study develops, defines, and applies a statistical technique for the compromise of confidential information in a statistical data base. Attribute Correlational Modeling (ACM) recognizes that the information contained in a statistical data base represents real world statistical phenomena. As such, ACM assumes correlational behavior among the database attributes. ACM proceeds to compromise confidential information through creation of a regression model, where the confidential attribute is treated as the dependent variable. The typical statistical data base may preclude the direct application of regression. In this scenario, the research introduces the notion of a synthetic data base, created through legitimate queries of the actual data base, and through proportional random variation of responses to these queries. The synthetic data base is constructed to resemble the actual data base as closely as possible in a statistical sense. ACM then applies regression analysis to the synthetic data base, and utilizes the derived model to estimate confidential information in the actual database.

Vortex dynamics and Lagrangian statistics in a model for active turbulence.

Science.gov (United States)

James, Martin; Wilczek, Michael

2018-02-14

Cellular suspensions such as dense bacterial flows exhibit a turbulence-like phase under certain conditions. We study this phenomenon of "active turbulence" statistically by using numerical tools. Following Wensink et al. (Proc. Natl. Acad. Sci. U.S.A. 109, 14308 (2012)), we model active turbulence by means of a generalized Navier-Stokes equation. Two-point velocity statistics of active turbulence, both in the Eulerian and the Lagrangian frame, is explored. We characterize the scale-dependent features of two-point statistics in this system. Furthermore, we extend this statistical study with measurements of vortex dynamics in this system. Our observations suggest that the large-scale statistics of active turbulence is close to Gaussian with sub-Gaussian tails.

Relevance of the c-statistic when evaluating risk-adjustment models in surgery.

Science.gov (United States)

Merkow, Ryan P; Hall, Bruce L; Cohen, Mark E; Dimick, Justin B; Wang, Edward; Chow, Warren B; Ko, Clifford Y; Bilimoria, Karl Y

2012-05-01

The measurement of hospital quality based on outcomes requires risk adjustment. The c-statistic is a popular tool used to judge model performance, but can be limited, particularly when evaluating specific operations in focused populations. Our objectives were to examine the interpretation and relevance of the c-statistic when used in models with increasingly similar case mix and to consider an alternative perspective on model calibration based on a graphical depiction of model fit. From the American College of Surgeons National Surgical Quality Improvement Program (2008-2009), patients were identified who underwent a general surgery procedure, and procedure groups were increasingly restricted: colorectal-all, colorectal-elective cases only, and colorectal-elective cancer cases only. Mortality and serious morbidity outcomes were evaluated using logistic regression-based risk adjustment, and model c-statistics and calibration curves were used to compare model performance. During the study period, 323,427 general, 47,605 colorectal-all, 39,860 colorectal-elective, and 21,680 colorectal cancer patients were studied. Mortality ranged from 1.0% in general surgery to 4.1% in the colorectal-all group, and serious morbidity ranged from 3.9% in general surgery to 12.4% in the colorectal-all procedural group. As case mix was restricted, c-statistics progressively declined from the general to the colorectal cancer surgery cohorts for both mortality and serious morbidity (mortality: 0.949 to 0.866; serious morbidity: 0.861 to 0.668). Calibration was evaluated graphically by examining predicted vs observed number of events over risk deciles. For both mortality and serious morbidity, there was no qualitative difference in calibration identified between the procedure groups. In the present study, we demonstrate how the c-statistic can become less informative and, in certain circumstances, can lead to incorrect model-based conclusions, as case mix is restricted and patients become

Benchmark validation of statistical models: Application to mediation analysis of imagery and memory.

Science.gov (United States)

MacKinnon, David P; Valente, Matthew J; Wurpts, Ingrid C

2018-03-29

This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Statistical transmutation in doped quantum dimer models.

Science.gov (United States)

Lamas, C A; Ralko, A; Cabra, D C; Poilblanc, D; Pujol, P

2012-07-06

We prove a "statistical transmutation" symmetry of doped quantum dimer models on the square, triangular, and kagome lattices: the energy spectrum is invariant under a simultaneous change of statistics (i.e., bosonic into fermionic or vice versa) of the holes and of the signs of all the dimer resonance loops. This exact transformation enables us to define the duality equivalence between doped quantum dimer Hamiltonians and provides the analytic framework to analyze dynamical statistical transmutations. We investigate numerically the doping of the triangular quantum dimer model with special focus on the topological Z(2) dimer liquid. Doping leads to four (instead of two for the square lattice) inequivalent families of Hamiltonians. Competition between phase separation, superfluidity, supersolidity, and fermionic phases is investigated in the four families.

Statistical model for prediction of hearing loss in patients receiving cisplatin chemotherapy.

Science.gov (United States)

Johnson, Andrew; Tarima, Sergey; Wong, Stuart; Friedland, David R; Runge, Christina L

2013-03-01

This statistical model might be used to predict cisplatin-induced hearing loss, particularly in patients undergoing concomitant radiotherapy. To create a statistical model based on pretreatment hearing thresholds to provide an individual probability for hearing loss from cisplatin therapy and, secondarily, to investigate the use of hearing classification schemes as predictive tools for hearing loss. Retrospective case-control study. Tertiary care medical center. A total of 112 subjects receiving chemotherapy and audiometric evaluation were evaluated for the study. Of these subjects, 31 met inclusion criteria for analysis. The primary outcome measurement was a statistical model providing the probability of hearing loss following the use of cisplatin chemotherapy. Fifteen of the 31 subjects had significant hearing loss following cisplatin chemotherapy. American Academy of Otolaryngology-Head and Neck Society and Gardner-Robertson hearing classification schemes revealed little change in hearing grades between pretreatment and posttreatment evaluations for subjects with or without hearing loss. The Chang hearing classification scheme could effectively be used as a predictive tool in determining hearing loss with a sensitivity of 73.33%. Pretreatment hearing thresholds were used to generate a statistical model, based on quadratic approximation, to predict hearing loss (C statistic = 0.842, cross-validated = 0.835). The validity of the model improved when only subjects who received concurrent head and neck irradiation were included in the analysis (C statistic = 0.91). A calculated cutoff of 0.45 for predicted probability has a cross-validated sensitivity and specificity of 80%. Pretreatment hearing thresholds can be used as a predictive tool for cisplatin-induced hearing loss, particularly with concomitant radiotherapy.

Monte Carlo based statistical power analysis for mediation models: methods and software.

Science.gov (United States)

Zhang, Zhiyong

2014-12-01

The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.

Textual information access statistical models

CERN Document Server

Gaussier, Eric

2013-01-01

This book presents statistical models that have recently been developed within several research communities to access information contained in text collections. The problems considered are linked to applications aiming at facilitating information access:- information extraction and retrieval;- text classification and clustering;- opinion mining;- comprehension aids (automatic summarization, machine translation, visualization).In order to give the reader as complete a description as possible, the focus is placed on the probability models used in the applications

Model for neural signaling leap statistics

International Nuclear Information System (INIS)

Chevrollier, Martine; Oria, Marcos

2011-01-01

We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5 0 C, awaken regime) and Levy statistics (T = 35.5 0 C, sleeping period), characterized by rare events of long range connections.

Model for neural signaling leap statistics

Science.gov (United States)

Chevrollier, Martine; Oriá, Marcos

2011-03-01

We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T = 37.5°C, awaken regime) and Lévy statistics (T = 35.5°C, sleeping period), characterized by rare events of long range connections.

Bayesian models a statistical primer for ecologists

CERN Document Server

Hobbs, N Thompson

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili

Statistical shear lag model - unraveling the size effect in hierarchical composites.

Science.gov (United States)

Wei, Xiaoding; Filleter, Tobin; Espinosa, Horacio D

2015-05-01

Numerous experimental and computational studies have established that the hierarchical structures encountered in natural materials, such as the brick-and-mortar structure observed in sea shells, are essential for achieving defect tolerance. Due to this hierarchy, the mechanical properties of natural materials have a different size dependence compared to that of typical engineered materials. This study aimed to explore size effects on the strength of bio-inspired staggered hierarchical composites and to define the influence of the geometry of constituents in their outstanding defect tolerance capability. A statistical shear lag model is derived by extending the classical shear lag model to account for the statistics of the constituents' strength. A general solution emerges from rigorous mathematical derivations, unifying the various empirical formulations for the fundamental link length used in previous statistical models. The model shows that the staggered arrangement of constituents grants composites a unique size effect on mechanical strength in contrast to homogenous continuous materials. The model is applied to hierarchical yarns consisting of double-walled carbon nanotube bundles to assess its predictive capabilities for novel synthetic materials. Interestingly, the model predicts that yarn gauge length does not significantly influence the yarn strength, in close agreement with experimental observations. Copyright © 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Development of a statistical shape model of multi-organ and its performance evaluation

International Nuclear Information System (INIS)

Nakada, Misaki; Shimizu, Akinobu; Kobatake, Hidefumi; Nawano, Shigeru

2010-01-01

Existing statistical shape modeling methods for an organ can not take into account the correlation between neighboring organs. This study focuses on a level set distribution model and proposes two modeling methods for multiple organs that can take into account the correlation between neighboring organs. The first method combines level set functions of multiple organs into a vector. Subsequently it analyses the distribution of the vectors of a training dataset by a principal component analysis and builds a multiple statistical shape model. Second method constructs a statistical shape model for each organ independently and assembles component scores of different organs in a training dataset so as to generate a vector. It analyses the distribution of the vectors of to build a statistical shape model of multiple organs. This paper shows results of applying the proposed methods trained by 15 abdominal CT volumes to unknown 8 CT volumes. (author)

Statistical models of petrol engines vehicles dynamics

Science.gov (United States)

Ilie, C. O.; Marinescu, M.; Alexa, O.; Vilău, R.; Grosu, D.

2017-10-01

This paper focuses on studying statistical models of vehicles dynamics. It was design and perform a one year testing program. There were used many same type cars with gasoline engines and different mileage. Experimental data were collected of onboard sensors and those on the engine test stand. A database containing data of 64th tests was created. Several mathematical modelling were developed using database and the system identification method. Each modelling is a SISO or a MISO linear predictive ARMAX (AutoRegressive-Moving-Average with eXogenous inputs) model. It represents a differential equation with constant coefficients. It were made 64th equations for each dependency like engine torque as output and engine’s load and intake manifold pressure, as inputs. There were obtained strings with 64 values for each type of model. The final models were obtained using average values of the coefficients. The accuracy of models was assessed.

Statistical modeling of geopressured geothermal reservoirs

Science.gov (United States)

Ansari, Esmail; Hughes, Richard; White, Christopher D.

2017-06-01

Identifying attractive candidate reservoirs for producing geothermal energy requires predictive models. In this work, inspectional analysis and statistical modeling are used to create simple predictive models for a line drive design. Inspectional analysis on the partial differential equations governing this design yields a minimum number of fifteen dimensionless groups required to describe the physics of the system. These dimensionless groups are explained and confirmed using models with similar dimensionless groups but different dimensional parameters. This study models dimensionless production temperature and thermal recovery factor as the responses of a numerical model. These responses are obtained by a Box-Behnken experimental design. An uncertainty plot is used to segment the dimensionless time and develop a model for each segment. The important dimensionless numbers for each segment of the dimensionless time are identified using the Boosting method. These selected numbers are used in the regression models. The developed models are reduced to have a minimum number of predictors and interactions. The reduced final models are then presented and assessed using testing runs. Finally, applications of these models are offered. The presented workflow is generic and can be used to translate the output of a numerical simulator into simple predictive models in other research areas involving numerical simulation.

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

Science.gov (United States)

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

Equilibrium statistical mechanics of lattice models

CERN Document Server

Lavis, David A

2015-01-01

Most interesting and difficult problems in equilibrium statistical mechanics concern models which exhibit phase transitions. For graduate students and more experienced researchers this book provides an invaluable reference source of approximate and exact solutions for a comprehensive range of such models. Part I contains background material on classical thermodynamics and statistical mechanics, together with a classification and survey of lattice models. The geometry of phase transitions is described and scaling theory is used to introduce critical exponents and scaling laws. An introduction is given to finite-size scaling, conformal invariance and Schramm—Loewner evolution. Part II contains accounts of classical mean-field methods. The parallels between Landau expansions and catastrophe theory are discussed and Ginzburg—Landau theory is introduced. The extension of mean-field theory to higher-orders is explored using the Kikuchi—Hijmans—De Boer hierarchy of approximations. In Part III the use of alge...

Statistical Power Analysis with Missing Data A Structural Equation Modeling Approach

CERN Document Server

Davey, Adam

2009-01-01

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research.  Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling.  It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types

Computational and Statistical Models: A Comparison for Policy Modeling of Childhood Obesity

Science.gov (United States)

Mabry, Patricia L.; Hammond, Ross; Ip, Edward Hak-Sing; Huang, Terry T.-K.

As systems science methodologies have begun to emerge as a set of innovative approaches to address complex problems in behavioral, social science, and public health research, some apparent conflicts with traditional statistical methodologies for public health have arisen. Computational modeling is an approach set in context that integrates diverse sources of data to test the plausibility of working hypotheses and to elicit novel ones. Statistical models are reductionist approaches geared towards proving the null hypothesis. While these two approaches may seem contrary to each other, we propose that they are in fact complementary and can be used jointly to advance solutions to complex problems. Outputs from statistical models can be fed into computational models, and outputs from computational models can lead to further empirical data collection and statistical models. Together, this presents an iterative process that refines the models and contributes to a greater understanding of the problem and its potential solutions. The purpose of this panel is to foster communication and understanding between statistical and computational modelers. Our goal is to shed light on the differences between the approaches and convey what kinds of research inquiries each one is best for addressing and how they can serve complementary (and synergistic) roles in the research process, to mutual benefit. For each approach the panel will cover the relevant "assumptions" and how the differences in what is assumed can foster misunderstandings. The interpretations of the results from each approach will be compared and contrasted and the limitations for each approach will be delineated. We will use illustrative examples from CompMod, the Comparative Modeling Network for Childhood Obesity Policy. The panel will also incorporate interactive discussions with the audience on the issues raised here.

Spherical Process Models for Global Spatial Statistics

KAUST Repository

Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.

2017-01-01

Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture

Model for neural signaling leap statistics

Energy Technology Data Exchange (ETDEWEB)

Chevrollier, Martine; Oria, Marcos, E-mail: oria@otica.ufpb.br [Laboratorio de Fisica Atomica e Lasers Departamento de Fisica, Universidade Federal da ParaIba Caixa Postal 5086 58051-900 Joao Pessoa, Paraiba (Brazil)

2011-03-01

We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5{sup 0}C, awaken regime) and Levy statistics (T = 35.5{sup 0}C, sleeping period), characterized by rare events of long range connections.

Statistical analysis of probabilistic models of software product lines with quantitative constraints

DEFF Research Database (Denmark)

Beek, M.H. ter; Legay, A.; Lluch Lafuente, Alberto

2015-01-01

We investigate the suitability of statistical model checking for the analysis of probabilistic models of software product lines with complex quantitative constraints and advanced feature installation options. Such models are specified in the feature-oriented language QFLan, a rich process algebra...... of certain behaviour to the expected average cost of products. This is supported by a Maude implementation of QFLan, integrated with the SMT solver Z3 and the distributed statistical model checker MultiVeStA. Our approach is illustrated with a bikes product line case study....

Analysis and Evaluation of Statistical Models for Integrated Circuits Design

Directory of Open Access Journals (Sweden)

Sáenz-Noval J.J.

2011-10-01

Full Text Available Statistical models for integrated circuits (IC allow us to estimate the percentage of acceptable devices in the batch before fabrication. Actually, Pelgrom is the statistical model most accepted in the industry; however it was derived from a micrometer technology, which does not guarantee reliability in nanometric manufacturing processes. This work considers three of the most relevant statistical models in the industry and evaluates their limitations and advantages in analog design, so that the designer has a better criterion to make a choice. Moreover, it shows how several statistical models can be used for each one of the stages and design purposes.

Understanding and forecasting polar stratospheric variability with statistical models

Directory of Open Access Journals (Sweden)

C. Blume

2012-07-01

Full Text Available The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA; a cluster method based on finite elements (FEM-VARX; a neural network, namely the multi-layer perceptron (MLP; and support vector regression (SVR. These methods model time series by incorporating all significant external factors simultaneously, including ENSO, QBO, the solar cycle, volcanoes, to then quantify their statistical importance. We show that variability in reanalysis data from 1980 to 2005 is successfully modeled. The period from 2005 to 2011 can be hindcasted to a certain extent, where MLP performs significantly better than the remaining models. However, variability remains that cannot be statistically hindcasted within the current framework, such as the unexpected major warming in January 2009. Finally, the statistical model with the best generalization performance is used to predict a winter 2011/12 with warm and weak vortex conditions. A vortex breakdown is predicted for late January, early February 2012.

Improved model for statistical alignment

Energy Technology Data Exchange (ETDEWEB)

Miklos, I.; Toroczkai, Z. (Zoltan)

2001-01-01

The statistical approach to molecular sequence evolution involves the stochastic modeling of the substitution, insertion and deletion processes. Substitution has been modeled in a reliable way for more than three decades by using finite Markov-processes. Insertion and deletion, however, seem to be more difficult to model, and thc recent approaches cannot acceptably deal with multiple insertions and deletions. A new method based on a generating function approach is introduced to describe the multiple insertion process. The presented algorithm computes the approximate joint probability of two sequences in 0(13) running time where 1 is the geometric mean of the sequence lengths.

A new method to determine the number of experimental data using statistical modeling methods

Energy Technology Data Exchange (ETDEWEB)

Jung, Jung-Ho; Kang, Young-Jin; Lim, O-Kaung; Noh, Yoojeong [Pusan National University, Busan (Korea, Republic of)

2017-06-15

For analyzing the statistical performance of physical systems, statistical characteristics of physical parameters such as material properties need to be estimated by collecting experimental data. For accurate statistical modeling, many such experiments may be required, but data are usually quite limited owing to the cost and time constraints of experiments. In this study, a new method for determining a rea- sonable number of experimental data is proposed using an area metric, after obtaining statistical models using the information on the underlying distribution, the Sequential statistical modeling (SSM) approach, and the Kernel density estimation (KDE) approach. The area metric is used as a convergence criterion to determine the necessary and sufficient number of experimental data to be acquired. The pro- posed method is validated in simulations, using different statistical modeling methods, different true models, and different convergence criteria. An example data set with 29 data describing the fatigue strength coefficient of SAE 950X is used for demonstrating the performance of the obtained statistical models that use a pre-determined number of experimental data in predicting the probability of failure for a target fatigue life.

Daily precipitation statistics in regional climate models

DEFF Research Database (Denmark)

Frei, Christoph; Christensen, Jens Hesselbjerg; Déqué, Michel

2003-01-01

An evaluation is undertaken of the statistics of daily precipitation as simulated by five regional climate models using comprehensive observations in the region of the European Alps. Four limited area models and one variable-resolution global model are considered, all with a grid spacing of 50 km...

Infinite Random Graphs as Statistical Mechanical Models

DEFF Research Database (Denmark)

Durhuus, Bergfinnur Jøgvan; Napolitano, George Maria

2011-01-01

We discuss two examples of infinite random graphs obtained as limits of finite statistical mechanical systems: a model of two-dimensional dis-cretized quantum gravity defined in terms of causal triangulated surfaces, and the Ising model on generic random trees. For the former model we describe a ...

Efficient Parallel Statistical Model Checking of Biochemical Networks

Directory of Open Access Journals (Sweden)

Paolo Ballarini

2009-12-01

Full Text Available We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture.

Probability of Detection (POD) as a statistical model for the validation of qualitative methods.

Science.gov (United States)

Wehling, Paul; LaBudde, Robert A; Brunelle, Sharon L; Nelson, Maria T

2011-01-01

A statistical model is presented for use in validation of qualitative methods. This model, termed Probability of Detection (POD), harmonizes the statistical concepts and parameters between quantitative and qualitative method validation. POD characterizes method response with respect to concentration as a continuous variable. The POD model provides a tool for graphical representation of response curves for qualitative methods. In addition, the model allows comparisons between candidate and reference methods, and provides calculations of repeatability, reproducibility, and laboratory effects from collaborative study data. Single laboratory study and collaborative study examples are given.

Development of 3D statistical mandible models for cephalometric measurements

International Nuclear Information System (INIS)

Kim, Sung Goo; Yi, Won Jin; Hwang, Soon Jung; Choi, Soon Chul; Lee, Sam Sun; Heo, Min Suk; Huh, Kyung Hoe; Kim, Tae Il; Hong, Helen; Yoo, Ji Hyun

2012-01-01

The aim of this study was to provide sex-matched three-dimensional (3D) statistical shape models of the mandible, which would provide cephalometric parameters for 3D treatment planning and cephalometric measurements in orthognathic surgery. The subjects used to create the 3D shape models of the mandible included 23 males and 23 females. The mandibles were segmented semi-automatically from 3D facial CT images. Each individual mandible shape was reconstructed as a 3D surface model, which was parameterized to establish correspondence between different individual surfaces. The principal component analysis (PCA) applied to all mandible shapes produced a mean model and characteristic models of variation. The cephalometric parameters were measured directly from the mean models to evaluate the 3D shape models. The means of the measured parameters were compared with those from other conventional studies. The male and female 3D statistical mean models were developed from 23 individual mandibles, respectively. The male and female characteristic shapes of variation produced by PCA showed a large variability included in the individual mandibles. The cephalometric measurements from the developed models were very close to those from some conventional studies. We described the construction of 3D mandibular shape models and presented the application of the 3D mandibular template in cephalometric measurements. Optimal reference models determined from variations produced by PCA could be used for craniofacial patients with various types of skeletal shape.

Development of 3D statistical mandible models for cephalometric measurements

Energy Technology Data Exchange (ETDEWEB)

Kim, Sung Goo; Yi, Won Jin; Hwang, Soon Jung; Choi, Soon Chul; Lee, Sam Sun; Heo, Min Suk; Huh, Kyung Hoe; Kim, Tae Il [School of Dentistry, Seoul National University, Seoul (Korea, Republic of); Hong, Helen; Yoo, Ji Hyun [Division of Multimedia Engineering, Seoul Women' s University, Seoul (Korea, Republic of)

2012-09-15

The aim of this study was to provide sex-matched three-dimensional (3D) statistical shape models of the mandible, which would provide cephalometric parameters for 3D treatment planning and cephalometric measurements in orthognathic surgery. The subjects used to create the 3D shape models of the mandible included 23 males and 23 females. The mandibles were segmented semi-automatically from 3D facial CT images. Each individual mandible shape was reconstructed as a 3D surface model, which was parameterized to establish correspondence between different individual surfaces. The principal component analysis (PCA) applied to all mandible shapes produced a mean model and characteristic models of variation. The cephalometric parameters were measured directly from the mean models to evaluate the 3D shape models. The means of the measured parameters were compared with those from other conventional studies. The male and female 3D statistical mean models were developed from 23 individual mandibles, respectively. The male and female characteristic shapes of variation produced by PCA showed a large variability included in the individual mandibles. The cephalometric measurements from the developed models were very close to those from some conventional studies. We described the construction of 3D mandibular shape models and presented the application of the 3D mandibular template in cephalometric measurements. Optimal reference models determined from variations produced by PCA could be used for craniofacial patients with various types of skeletal shape.

Multimesonic decays of charmonium states in the statistical quark model

International Nuclear Information System (INIS)

Montvay, I.; Toth, J.D.

1978-01-01

The data known at present of multimesonic decays of chi and psi states are fitted in a statistical quark model, in which the matrix elements are assumed to be constant and resonances as well as both strong and second order electromagnetic processes are taken into account. The experimental data are well reproduced by the model. Unknown branching ratios for the rest of multimesonic channels are predicted. The fit leaves about 40% for baryonic and radiative channels in the case of J/psi(3095). The fitted parameters of the J/psi decays are used to predict the mesonic decays of the pseudoscalar eta c. The statistical quark model seems to allow the calculation of competitive multiparticle processes for the studied decays. (D.P.)

Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

Directory of Open Access Journals (Sweden)

Seyed Mehran Kazemi

2018-02-01

Full Text Available The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been mostly developed and studied in isolation for many years, with few works attempting at understanding the relationship among them or combining them. In this article, we study the relationship between the path ranking algorithm (PRA, one of the most well-known relational learning methods in the graph random walk paradigm, and relational logistic regression (RLR, one of the recent developments in weighted rule learning. We provide a simple way to normalize relations and prove that relational logistic regression using normalized relations generalizes the path ranking algorithm. This result provides a better understanding of relational learning, especially for the weighted rule learning and graph random walk paradigms. It opens up the possibility of using the more flexible RLR rules within PRA models and even generalizing both by including normalized and unnormalized relations in the same model.

Statistical mechanics of directed models of polymers in the square lattice

International Nuclear Information System (INIS)

Rensburg, E J Janse van

2003-01-01

Directed square lattice models of polymers and vesicles have received considerable attention in the recent mathematical and physical sciences literature. These are idealized geometric directed lattice models introduced to study phase behaviour in polymers, and include Dyck paths, partially directed paths, directed trees and directed vesicles models. Directed models are closely related to models studied in the combinatorics literature (and are often exactly solvable). They are also simplified versions of a number of statistical mechanics models, including the self-avoiding walk, lattice animals and lattice vesicles. The exchange of approaches and ideas between statistical mechanics and combinatorics have considerably advanced the description and understanding of directed lattice models, and this will be explored in this review. The combinatorial nature of directed lattice path models makes a study using generating function approaches most natural. In contrast, the statistical mechanics approach would introduce partition functions and free energies, and then investigate these using the general framework of critical phenomena. Generating function and statistical mechanics approaches are closely related. For example, questions regarding the limiting free energy may be approached by considering the radius of convergence of a generating function, and the scaling properties of thermodynamic quantities are related to the asymptotic properties of the generating function. In this review the methods for obtaining generating functions and determining free energies in directed lattice path models of linear polymers is presented. These methods include decomposition methods leading to functional recursions, as well as the Temperley method (that is implemented by creating a combinatorial object, one slice at a time). A constant term formulation of the generating function will also be reviewed. The thermodynamic features and critical behaviour in models of directed paths may be

Speech emotion recognition based on statistical pitch model

Institute of Scientific and Technical Information of China (English)

WANG Zhiping; ZHAO Li; ZOU Cairong

2006-01-01

A modified Parzen-window method, which keep high resolution in low frequencies and keep smoothness in high frequencies, is proposed to obtain statistical model. Then, a gender classification method utilizing the statistical model is proposed, which have a 98% accuracy of gender classification while long sentence is dealt with. By separation the male voice and female voice, the mean and standard deviation of speech training samples with different emotion are used to create the corresponding emotion models. Then the Bhattacharyya distance between the test sample and statistical models of pitch, are utilized for emotion recognition in speech.The normalization of pitch for the male voice and female voice are also considered, in order to illustrate them into a uniform space. Finally, the speech emotion recognition experiment based on K Nearest Neighbor shows that, the correct rate of 81% is achieved, where it is only 73.85%if the traditional parameters are utilized.

Statistically Modeling I-V Characteristics of CNT-FET with LASSO

Science.gov (United States)

Ma, Dongsheng; Ye, Zuochang; Wang, Yan

2017-08-01

With the advent of internet of things (IOT), the need for studying new material and devices for various applications is increasing. Traditionally we build compact models for transistors on the basis of physics. But physical models are expensive and need a very long time to adjust for non-ideal effects. As the vision for the application of many novel devices is not certain or the manufacture process is not mature, deriving generalized accurate physical models for such devices is very strenuous, whereas statistical modeling is becoming a potential method because of its data oriented property and fast implementation. In this paper, one classical statistical regression method, LASSO, is used to model the I-V characteristics of CNT-FET and a pseudo-PMOS inverter simulation based on the trained model is implemented in Cadence. The normalized relative mean square prediction error of the trained model versus experiment sample data and the simulation results show that the model is acceptable for digital circuit static simulation. And such modeling methodology can extend to general devices.

Statistical modelling of citation exchange between statistics journals.

Science.gov (United States)

Varin, Cristiano; Cattelan, Manuela; Firth, David

2016-01-01

Rankings of scholarly journals based on citation data are often met with scepticism by the scientific community. Part of the scepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of researchers. The paper focuses on analysis of the table of cross-citations among a selection of statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care to avoid potential overinterpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's research assessment exercise shows strong correlation at aggregate level between assessed research quality and journal citation 'export scores' within the discipline of statistics.

TRAN-STAT: statistics for environmental studies

International Nuclear Information System (INIS)

Gilbert, R.O.

1984-09-01

This issue of TRAN-STAT discusses statistical methods for assessing the uncertainty in predictions of pollutant transport models, particularly for radionuclides. Emphasis is placed on radionuclide transport models but the statistical assessment techniques also apply in general to other types of pollutants. The report begins with an outline of why an assessment of prediction uncertainties is important. This is followed by an introduction to several methods currently used in these assessments. This in turn is followed by more detailed discussion of the methods, including examples. 43 references, 2 figures

Statistics of a neuron model driven by asymmetric colored noise.

Science.gov (United States)

Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin

2015-02-01

Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.

Statistical validation of normal tissue complication probability models.

Science.gov (United States)

Xu, Cheng-Jian; van der Schaaf, Arjen; Van't Veld, Aart A; Langendijk, Johannes A; Schilstra, Cornelis

2012-09-01

To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use. Copyright © 2012 Elsevier Inc. All rights reserved.

Statistical Validation of Normal Tissue Complication Probability Models

Energy Technology Data Exchange (ETDEWEB)

Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Veld, Aart A. van' t; Langendijk, Johannes A. [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schilstra, Cornelis [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Radiotherapy Institute Friesland, Leeuwarden (Netherlands)

2012-09-01

Purpose: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. Methods and Materials: A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Results: Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Conclusion: Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use.

Advances in statistical models for data analysis

CERN Document Server

Minerva, Tommaso; Vichi, Maurizio

2015-01-01

This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.

Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates

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Müller, M. F.; Thompson, S. E.

2015-09-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.

Models for probability and statistical inference theory and applications

CERN Document Server

Stapleton, James H

2007-01-01

This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses mo...

Fluctuations and correlations in statistical models of hadron production

International Nuclear Information System (INIS)

Gorenstein, M. I.

2012-01-01

An extension of the standard concept of the statistical ensembles is suggested. Namely, the statistical ensembles with extensive quantities fluctuating according to an externally given distribution are introduced. Applications in the statistical models of multiple hadron production in high energy physics are discussed.

Statistical inference to advance network models in epidemiology.

Science.gov (United States)

Welch, David; Bansal, Shweta; Hunter, David R

2011-03-01

Contact networks are playing an increasingly important role in the study of epidemiology. Most of the existing work in this area has focused on considering the effect of underlying network structure on epidemic dynamics by using tools from probability theory and computer simulation. This work has provided much insight on the role that heterogeneity in host contact patterns plays on infectious disease dynamics. Despite the important understanding afforded by the probability and simulation paradigm, this approach does not directly address important questions about the structure of contact networks such as what is the best network model for a particular mode of disease transmission, how parameter values of a given model should be estimated, or how precisely the data allow us to estimate these parameter values. We argue that these questions are best answered within a statistical framework and discuss the role of statistical inference in estimating contact networks from epidemiological data. Copyright © 2011 Elsevier B.V. All rights reserved.

GALEX-SDSS CATALOGS FOR STATISTICAL STUDIES

International Nuclear Information System (INIS)

Budavari, Tamas; Heinis, Sebastien; Szalay, Alexander S.; Nieto-Santisteban, Maria; Bianchi, Luciana; Gupchup, Jayant; Shiao, Bernie; Smith, Myron; Chang Ruixiang; Kauffmann, Guinevere; Morrissey, Patrick; Wyder, Ted K.; Martin, D. Christopher; Barlow, Tom A.; Forster, Karl; Friedman, Peter G.; Schiminovich, David; Milliard, Bruno; Donas, Jose; Seibert, Mark

2009-01-01

We present a detailed study of the Galaxy Evolution Explorer's (GALEX) photometric catalogs with special focus on the statistical properties of the All-sky and Medium Imaging Surveys. We introduce the concept of primaries to resolve the issue of multiple detections and follow a geometric approach to define clean catalogs with well understood selection functions. We cross-identify the GALEX sources (GR2+3) with Sloan Digital Sky Survey (SDSS; DR6) observations, which indirectly provides an invaluable insight into the astrometric model of the UV sources and allows us to revise the band merging strategy. We derive the formal description of the GALEX footprints as well as their intersections with the SDSS coverage along with analytic calculations of their areal coverage. The crossmatch catalogs are made available for the public. We conclude by illustrating the implementation of typical selection criteria in SQL for catalog subsets geared toward statistical analyses, e.g., correlation and luminosity function studies.

Growth curve models and statistical diagnostics

CERN Document Server

Pan, Jian-Xin

2002-01-01

Growth-curve models are generalized multivariate analysis-of-variance models. These models are especially useful for investigating growth problems on short times in economics, biology, medical research, and epidemiology. This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. The authors provide complete proofs of theorems as well as practical data sets and MATLAB code.

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Science.gov (United States)

Makowski, David; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean-Louis; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Chritian;

Statistical physics of medical diagnostics: Study of a probabilistic model.

Science.gov (United States)

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Statistical physics of medical diagnostics: Study of a probabilistic model

Science.gov (United States)

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Advanced data analysis in neuroscience integrating statistical and computational models

CERN Document Server

Durstewitz, Daniel

2017-01-01

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering.  Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerfu...

Studies in Theoretical and Applied Statistics

CERN Document Server

Pratesi, Monica; Ruiz-Gazen, Anne

2018-01-01

This book includes a wide selection of the papers presented at the 48th Scientific Meeting of the Italian Statistical Society (SIS2016), held in Salerno on 8-10 June 2016. Covering a wide variety of topics ranging from modern data sources and survey design issues to measuring sustainable development, it provides a comprehensive overview of the current Italian scientific research in the fields of open data and big data in public administration and official statistics, survey sampling, ordinal and symbolic data, statistical models and methods for network data, time series forecasting, spatial analysis, environmental statistics, economic and financial data analysis, statistics in the education system, and sustainable development. Intended for researchers interested in theoretical and empirical issues, this volume provides interesting starting points for further research.

Statistical Language Models and Information Retrieval: Natural Language Processing Really Meets Retrieval

NARCIS (Netherlands)

Hiemstra, Djoerd; de Jong, Franciska M.G.

2001-01-01

Traditionally, natural language processing techniques for information retrieval have always been studied outside the framework of formal models of information retrieval. In this article, we introduce a new formal model of information retrieval based on the application of statistical language models.

Bayesian Sensitivity Analysis of Statistical Models with Missing Data.

Science.gov (United States)

Zhu, Hongtu; Ibrahim, Joseph G; Tang, Niansheng

2014-04-01

Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.

Statistics of excitations in the electron glass model

Science.gov (United States)

Palassini, Matteo

2011-03-01

We study the statistics of elementary excitations in the classical electron glass model of localized electrons interacting via the unscreened Coulomb interaction in the presence of disorder. We reconsider the long-standing puzzle of the exponential suppression of the single-particle density of states near the Fermi level, by measuring accurately the density of states of charged and electron-hole pair excitations via finite temperature Monte Carlo simulation and zero-temperature relaxation. We also investigate the statistics of large charge rearrangements after a perturbation of the system, which may shed some light on the slow relaxation and glassy phenomena recently observed in a variety of Anderson insulators. In collaboration with Martin Goethe.

How to practise Bayesian statistics outside the Bayesian church: What philosophy for Bayesian statistical modelling?

NARCIS (Netherlands)

Borsboom, D.; Haig, B.D.

2013-01-01

Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science

A statistical model for interpreting computerized dynamic posturography data

Science.gov (United States)

Feiveson, Alan H.; Metter, E. Jeffrey; Paloski, William H.

2002-01-01

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment

Directory of Open Access Journals (Sweden)

Yuze Wang

2018-04-01

Full Text Available A deep understanding of multipath characteristics is essential to design signal simulators and receivers in global navigation satellite system applications. As a new constellation is deployed and more applications occur in the urban environment, the statistical multipath models of navigation signal need further study. In this paper, we present statistical distribution models of multipath time delay, multipath power attenuation, and multipath fading frequency based on the experimental data in the urban canyon environment. The raw data of multipath characteristics are obtained by processing real navigation signal to study the statistical distribution. By fitting the statistical data, it shows that the probability distribution of time delay follows a gamma distribution which is related to the waiting time of Poisson distributed events. The fading frequency follows an exponential distribution, and the mean of multipath power attenuation decreases linearly with an increasing time delay. In addition, the detailed statistical characteristics for different elevations and orbits satellites is studied, and the parameters of each distribution are quite different. The research results give useful guidance for navigation simulator and receiver designers.

Statistical modeling of static strengths of nuclear graphites with relevance to structural design

International Nuclear Information System (INIS)

Arai, Taketoshi

1992-02-01

Use of graphite materials for structural members poses a problem as to how to take into account of statistical properties of static strength, especially tensile fracture stresses, in component structural design. The present study concerns comprehensive examinations on statistical data base and modelings on nuclear graphites. First, the report provides individual samples and their analyses on strengths of IG-110 and PGX graphites for HTTR components. Those statistical characteristics on other HTGR graphites are also exemplified from the literature. Most of statistical distributions of individual samples are found to be approximately normal. The goodness of fit to normal distributions is more satisfactory with larger sample sizes. Molded and extruded graphites, however, possess a variety of statistical properties depending of samples from different with-in-log locations and/or different orientations. Second, the previous statistical models including the Weibull theory are assessed from the viewpoint of applicability to design procedures. This leads to a conclusion that the Weibull theory and its modified ones are satisfactory only for limited parts of tensile fracture behavior. They are not consistent for whole observations. Only normal statistics are justifiable as practical approaches to discuss specified minimum ultimate strengths as statistical confidence limits for individual samples. Third, the assessment of various statistical models emphasizes the need to develop advanced analytical ones which should involve modeling of microstructural features of actual graphite materials. Improvements of other structural design methodologies are also presented. (author)

Meta-analysis for diagnostic accuracy studies: a new statistical model using beta-binomial distributions and bivariate copulas.

Science.gov (United States)

Kuss, Oliver; Hoyer, Annika; Solms, Alexander

2014-01-15

There are still challenges when meta-analyzing data from studies on diagnostic accuracy. This is mainly due to the bivariate nature of the response where information on sensitivity and specificity must be summarized while accounting for their correlation within a single trial. In this paper, we propose a new statistical model for the meta-analysis for diagnostic accuracy studies. This model uses beta-binomial distributions for the marginal numbers of true positives and true negatives and links these margins by a bivariate copula distribution. The new model comes with all the features of the current standard model, a bivariate logistic regression model with random effects, but has the additional advantages of a closed likelihood function and a larger flexibility for the correlation structure of sensitivity and specificity. In a simulation study, which compares three copula models and two implementations of the standard model, the Plackett and the Gauss copula do rarely perform worse but frequently better than the standard model. We use an example from a meta-analysis to judge the diagnostic accuracy of telomerase (a urinary tumor marker) for the diagnosis of primary bladder cancer for illustration. Copyright © 2013 John Wiley & Sons, Ltd.

Cellular automata and statistical mechanical models

International Nuclear Information System (INIS)

Rujan, P.

1987-01-01

The authors elaborate on the analogy between the transfer matrix of usual lattice models and the master equation describing the time development of cellular automata. Transient and stationary properties of probabilistic automata are linked to surface and bulk properties, respectively, of restricted statistical mechanical systems. It is demonstrated that methods of statistical physics can be successfully used to describe the dynamic and the stationary behavior of such automata. Some exact results are derived, including duality transformations, exact mappings, disorder, and linear solutions. Many examples are worked out in detail to demonstrate how to use statistical physics in order to construct cellular automata with desired properties. This approach is considered to be a first step toward the design of fully parallel, probabilistic systems whose computational abilities rely on the cooperative behavior of their components

Improving statistical reasoning theoretical models and practical implications

CERN Document Server

Sedlmeier, Peter

1999-01-01

This book focuses on how statistical reasoning works and on training programs that can exploit people''s natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.

Atmospheric corrosion: statistical validation of models

International Nuclear Information System (INIS)

Diaz, V.; Martinez-Luaces, V.; Guineo-Cobs, G.

2003-01-01

In this paper we discuss two different methods for validation of regression models, applied to corrosion data. One of them is based on the correlation coefficient and the other one is the statistical test of lack of fit. Both methods are used here to analyse fitting of bi logarithmic model in order to predict corrosion for very low carbon steel substrates in rural and urban-industrial atmospheres in Uruguay. Results for parameters A and n of the bi logarithmic model are reported here. For this purpose, all repeated values were used instead of using average values as usual. Modelling is carried out using experimental data corresponding to steel substrates under the same initial meteorological conditions ( in fact, they are put in the rack at the same time). Results of correlation coefficient are compared with the lack of it tested at two different signification levels (α=0.01 and α=0.05). Unexpected differences between them are explained and finally, it is possible to conclude, at least in the studied atmospheres, that the bi logarithmic model does not fit properly the experimental data. (Author) 18 refs

The l z ( p ) * Person-Fit Statistic in an Unfolding Model Context.

Science.gov (United States)

Tendeiro, Jorge N

2017-01-01

Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded unfolding model is used. Results from a simulation study indicate that the person-fit statistic performed relatively well in detecting midpoint response style patterns and not so well in detecting extreme response style patterns.

Solar radiation data - statistical analysis and simulation models

Energy Technology Data Exchange (ETDEWEB)

Mustacchi, C; Cena, V; Rocchi, M; Haghigat, F

1984-01-01

The activities consisted in collecting meteorological data on magnetic tape for ten european locations (with latitudes ranging from 42/sup 0/ to 56/sup 0/ N), analysing the multi-year sequences, developing mathematical models to generate synthetic sequences having the same statistical properties of the original data sets, and producing one or more Short Reference Years (SRY's) for each location. The meteorological parameters examinated were (for all the locations) global + diffuse radiation on horizontal surface, dry bulb temperature, sunshine duration. For some of the locations additional parameters were available, namely, global, beam and diffuse radiation on surfaces other than horizontal, wet bulb temperature, wind velocity, cloud type, cloud cover. The statistical properties investigated were mean, variance, autocorrelation, crosscorrelation with selected parameters, probability density function. For all the meteorological parameters, various mathematical models were built: linear regression, stochastic models of the AR and the DAR type. In each case, the model with the best statistical behaviour was selected for the production of a SRY for the relevant parameter/location.

Use of statistical study methods for the analysis of the results of the imitation modeling of radiation transfer

Science.gov (United States)

Alekseenko, M. A.; Gendrina, I. Yu.

2017-11-01

Recently, due to the abundance of various types of observational data in the systems of vision through the atmosphere and the need for their processing, the use of various methods of statistical research in the study of such systems as correlation-regression analysis, dynamic series, variance analysis, etc. is actual. We have attempted to apply elements of correlation-regression analysis for the study and subsequent prediction of the patterns of radiation transfer in these systems same as in the construction of radiation models of the atmosphere. In this paper, we present some results of statistical processing of the results of numerical simulation of the characteristics of vision systems through the atmosphere obtained with the help of a special software package.1

Falling in the elderly: Do statistical models matter for performance criteria of fall prediction? Results from two large population-based studies.

Science.gov (United States)

Kabeshova, Anastasiia; Launay, Cyrille P; Gromov, Vasilii A; Fantino, Bruno; Levinoff, Elise J; Allali, Gilles; Beauchet, Olivier

2016-01-01

To compare performance criteria (i.e., sensitivity, specificity, positive predictive value, negative predictive value, area under receiver operating characteristic curve and accuracy) of linear and non-linear statistical models for fall risk in older community-dwellers. Participants were recruited in two large population-based studies, "Prévention des Chutes, Réseau 4" (PCR4, n=1760, cross-sectional design, retrospective collection of falls) and "Prévention des Chutes Personnes Agées" (PCPA, n=1765, cohort design, prospective collection of falls). Six linear statistical models (i.e., logistic regression, discriminant analysis, Bayes network algorithm, decision tree, random forest, boosted trees), three non-linear statistical models corresponding to artificial neural networks (multilayer perceptron, genetic algorithm and neuroevolution of augmenting topologies [NEAT]) and the adaptive neuro fuzzy interference system (ANFIS) were used. Falls ≥1 characterizing fallers and falls ≥2 characterizing recurrent fallers were used as outcomes. Data of studies were analyzed separately and together. NEAT and ANFIS had better performance criteria compared to other models. The highest performance criteria were reported with NEAT when using PCR4 database and falls ≥1, and with both NEAT and ANFIS when pooling data together and using falls ≥2. However, sensitivity and specificity were unbalanced. Sensitivity was higher than specificity when identifying fallers, whereas the converse was found when predicting recurrent fallers. Our results showed that NEAT and ANFIS were non-linear statistical models with the best performance criteria for the prediction of falls but their sensitivity and specificity were unbalanced, underscoring that models should be used respectively for the screening of fallers and the diagnosis of recurrent fallers. Copyright © 2015 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

Directory of Open Access Journals (Sweden)

Wuyang Cheng

2014-01-01

Full Text Available We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI and Hang Seng Index (HSI are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

Statistical Model Checking for Biological Systems

DEFF Research Database (Denmark)

David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel

2014-01-01

Statistical Model Checking (SMC) is a highly scalable simulation-based verification approach for testing and estimating the probability that a stochastic system satisfies a given linear temporal property. The technique has been applied to (discrete and continuous time) Markov chains, stochastic...

Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models.

Science.gov (United States)

Nolte, Daniel; Tsang, Chui Kit; Zhang, Kai Yu; Ding, Ziyun; Kedgley, Angela E; Bull, Anthony M J

2016-10-03

Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods. Copyright © 2016 The Author. Published by Elsevier Ltd.. All rights reserved.

A statistical skull geometry model for children 0-3 years old.

Directory of Open Access Journals (Sweden)

Zhigang Li

Full Text Available Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO. To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0-3 YO population. In this study, head CT scans from fifty-six 0-3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.

A statistical skull geometry model for children 0-3 years old.

Science.gov (United States)

Li, Zhigang; Park, Byoung-Keon; Liu, Weiguo; Zhang, Jinhuan; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen

2015-01-01

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0-3 YO population. In this study, head CT scans from fifty-six 0-3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

Science.gov (United States)

Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

2014-01-01

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

Statistical models based on conditional probability distributions

International Nuclear Information System (INIS)

Narayanan, R.S.

1991-10-01

We present a formulation of statistical mechanics models based on conditional probability distribution rather than a Hamiltonian. We show that it is possible to realize critical phenomena through this procedure. Closely linked with this formulation is a Monte Carlo algorithm, in which a configuration generated is guaranteed to be statistically independent from any other configuration for all values of the parameters, in particular near the critical point. (orig.)

A statistical model for mapping morphological shape

Directory of Open Access Journals (Sweden)

Li Jiahan

2010-07-01

Full Text Available Abstract Background Living things come in all shapes and sizes, from bacteria, plants, and animals to humans. Knowledge about the genetic mechanisms for biological shape has far-reaching implications for a range spectrum of scientific disciplines including anthropology, agriculture, developmental biology, evolution and biomedicine. Results We derived a statistical model for mapping specific genes or quantitative trait loci (QTLs that control morphological shape. The model was formulated within the mixture framework, in which different types of shape are thought to result from genotypic discrepancies at a QTL. The EM algorithm was implemented to estimate QTL genotype-specific shapes based on a shape correspondence analysis. Computer simulation was used to investigate the statistical property of the model. Conclusion By identifying specific QTLs for morphological shape, the model developed will help to ask, disseminate and address many major integrative biological and genetic questions and challenges in the genetic control of biological shape and function.

Statistical methods for mechanistic model validation: Salt Repository Project

International Nuclear Information System (INIS)

Eggett, D.L.

1988-07-01

As part of the Department of Energy's Salt Repository Program, Pacific Northwest Laboratory (PNL) is studying the emplacement of nuclear waste containers in a salt repository. One objective of the SRP program is to develop an overall waste package component model which adequately describes such phenomena as container corrosion, waste form leaching, spent fuel degradation, etc., which are possible in the salt repository environment. The form of this model will be proposed, based on scientific principles and relevant salt repository conditions with supporting data. The model will be used to predict the future characteristics of the near field environment. This involves several different submodels such as the amount of time it takes a brine solution to contact a canister in the repository, how long it takes a canister to corrode and expose its contents to the brine, the leach rate of the contents of the canister, etc. These submodels are often tested in a laboratory and should be statistically validated (in this context, validate means to demonstrate that the model adequately describes the data) before they can be incorporated into the waste package component model. This report describes statistical methods for validating these models. 13 refs., 1 fig., 3 tabs

Statistical model selection with “Big Data”

Directory of Open Access Journals (Sweden)

Jurgen A. Doornik

2015-12-01

Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.

Workshop on Model Uncertainty and its Statistical Implications

CERN Document Server

1988-01-01

In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.

Optimizing refiner operation with statistical modelling

Energy Technology Data Exchange (ETDEWEB)

Broderick, G [Noranda Research Centre, Pointe Claire, PQ (Canada)

1997-02-01

The impact of refining conditions on the energy efficiency of the process and on the handsheet quality of a chemi-mechanical pulp was studied as part of a series of pilot scale refining trials. Statistical models of refiner performance were constructed from these results and non-linear optimization of process conditions were conducted. Optimization results indicated that increasing the ratio of specific energy applied in the first stage led to a reduction of some 15 per cent in the total energy requirement. The strategy can also be used to obtain significant increases in pulp quality for a given energy input. 20 refs., 6 tabs.

Probability density function shape sensitivity in the statistical modeling of turbulent particle dispersion

Science.gov (United States)

Litchford, Ron J.; Jeng, San-Mou

1992-01-01

The performance of a recently introduced statistical transport model for turbulent particle dispersion is studied here for rigid particles injected into a round turbulent jet. Both uniform and isosceles triangle pdfs are used. The statistical sensitivity to parcel pdf shape is demonstrated.

Kolmogorov complexity, pseudorandom generators and statistical models testing

Czech Academy of Sciences Publication Activity Database

Šindelář, Jan; Boček, Pavel

2002-01-01

Roč. 38, č. 6 (2002), s. 747-759 ISSN 0023-5954 R&D Projects: GA ČR GA102/99/1564 Institutional research plan: CEZ:AV0Z1075907 Keywords : Kolmogorov complexity * pseudorandom generators * statistical models testing Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.341, year: 2002

Uniting statistical and individual-based approaches for animal movement modelling.

Science.gov (United States)

Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel

2014-01-01

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

Visualization of the variability of 3D statistical shape models by animation.

Science.gov (United States)

Lamecker, Hans; Seebass, Martin; Lange, Thomas; Hege, Hans-Christian; Deuflhard, Peter

2004-01-01

Models of the 3D shape of anatomical objects and the knowledge about their statistical variability are of great benefit in many computer assisted medical applications like images analysis, therapy or surgery planning. Statistical model of shapes have successfully been applied to automate the task of image segmentation. The generation of 3D statistical shape models requires the identification of corresponding points on two shapes. This remains a difficult problem, especially for shapes of complicated topology. In order to interpret and validate variations encoded in a statistical shape model, visual inspection is of great importance. This work describes the generation and interpretation of statistical shape models of the liver and the pelvic bone.

Quantum statistical model of nuclear multifragmentation in the canonical ensemble method

International Nuclear Information System (INIS)

Toneev, V.D.; Ploszajczak, M.; Parvant, A.S.; Toneev, V.D.; Parvant, A.S.

1999-01-01

A quantum statistical model of nuclear multifragmentation is proposed. The recurrence equation method used the canonical ensemble makes the model solvable and transparent to physical assumptions and allows to get results without involving the Monte Carlo technique. The model exhibits the first order phase transition. Quantum statistics effects are clearly seen on the microscopic level of occupation numbers but are almost washed out for global thermodynamic variables and the averaged observables studied. In the latter case, the recurrence relations for multiplicity distributions of both intermediate-mass and all fragments are derived and the specific changes in the shape of multiplicity distributions in the narrow region of the transition temperature is stressed. The temperature domain favorable to search for the HBT effect is noted. (authors)

Quantum statistical model of nuclear multifragmentation in the canonical ensemble method

Energy Technology Data Exchange (ETDEWEB)

Toneev, V.D.; Ploszajczak, M. [Grand Accelerateur National d' Ions Lourds (GANIL), 14 - Caen (France); Parvant, A.S. [Institute of Applied Physics, Moldova Academy of Sciences, MD Moldova (Ukraine); Parvant, A.S. [Joint Institute for Nuclear Research, Bogoliubov Lab. of Theoretical Physics, Dubna (Russian Federation)

1999-07-01

A quantum statistical model of nuclear multifragmentation is proposed. The recurrence equation method used the canonical ensemble makes the model solvable and transparent to physical assumptions and allows to get results without involving the Monte Carlo technique. The model exhibits the first order phase transition. Quantum statistics effects are clearly seen on the microscopic level of occupation numbers but are almost washed out for global thermodynamic variables and the averaged observables studied. In the latter case, the recurrence relations for multiplicity distributions of both intermediate-mass and all fragments are derived and the specific changes in the shape of multiplicity distributions in the narrow region of the transition temperature is stressed. The temperature domain favorable to search for the HBT effect is noted. (authors)

CONFIDENCE LEVELS AND/VS. STATISTICAL HYPOTHESIS TESTING IN STATISTICAL ANALYSIS. CASE STUDY

Directory of Open Access Journals (Sweden)

ILEANA BRUDIU

2009-05-01

Full Text Available Estimated parameters with confidence intervals and testing statistical assumptions used in statistical analysis to obtain conclusions on research from a sample extracted from the population. Paper to the case study presented aims to highlight the importance of volume of sample taken in the study and how this reflects on the results obtained when using confidence intervals and testing for pregnant. If statistical testing hypotheses not only give an answer "yes" or "no" to some questions of statistical estimation using statistical confidence intervals provides more information than a test statistic, show high degree of uncertainty arising from small samples and findings build in the "marginally significant" or "almost significant (p very close to 0.05.

An explicit statistical model of learning lexical segmentation using multiple cues

NARCIS (Netherlands)

Çöltekin, Ça ̆grı; Nerbonne, John; Lenci, Alessandro; Padró, Muntsa; Poibeau, Thierry; Villavicencio, Aline

2014-01-01

This paper presents an unsupervised and incremental model of learning segmentation that combines multiple cues whose use by children and adults were attested by experimental studies. The cues we exploit in this study are predictability statistics, phonotactics, lexical stress and partial lexical

Multivariate statistical modelling based on generalized linear models

CERN Document Server

Fahrmeir, Ludwig

1994-01-01

This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...

Linear mixed models a practical guide using statistical software

CERN Document Server

West, Brady T; Galecki, Andrzej T

2006-01-01

Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on bo

Active Learning with Statistical Models.

Science.gov (United States)

1995-01-01

Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

Parametric analysis of the statistical model of the stick-slip process

Science.gov (United States)

Lima, Roberta; Sampaio, Rubens

2017-06-01

In this paper it is performed a parametric analysis of the statistical model of the response of a dry-friction oscillator. The oscillator is a spring-mass system which moves over a base with a rough surface. Due to this roughness, the mass is subject to a dry-frictional force modeled as a Coulomb friction. The system is stochastically excited by an imposed bang-bang base motion. The base velocity is modeled by a Poisson process for which a probabilistic model is fully specified. The excitation induces in the system stochastic stick-slip oscillations. The system response is composed by a random sequence alternating stick and slip-modes. With realizations of the system, a statistical model is constructed for this sequence. In this statistical model, the variables of interest of the sequence are modeled as random variables, as for example, the number of time intervals in which stick or slip occur, the instants at which they begin, and their duration. Samples of the system response are computed by integration of the dynamic equation of the system using independent samples of the base motion. Statistics and histograms of the random variables which characterize the stick-slip process are estimated for the generated samples. The objective of the paper is to analyze how these estimated statistics and histograms vary with the system parameters, i.e., to make a parametric analysis of the statistical model of the stick-slip process.

Statistical modelling of monthly mean sea level at coastal tide gauge stations along the Indian subcontinent

Digital Repository Service at National Institute of Oceanography (India)

Srinivas, K.; Das, V.K.; DineshKumar, P.K.

This study investigates the suitability of statistical models for their predictive potential for the monthly mean sea level at different stations along the west and east coasts of the Indian subcontinent. Statistical modelling of the monthly mean...

Decoding β-decay systematics: A global statistical model for β- half-lives

International Nuclear Information System (INIS)

Costiris, N. J.; Mavrommatis, E.; Gernoth, K. A.; Clark, J. W.

2009-01-01

Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical modeling is pursued within the general framework of machine learning theory, we implement advances in training algorithms designed to improve generalization, in application to the problem of reproducing and predicting the half-lives of nuclear ground states that decay 100% by the β - mode. More specifically, fully connected, multilayer feed-forward artificial neural network models are developed using the Levenberg-Marquardt optimization algorithm together with Bayesian regularization and cross-validation. The predictive performance of models emerging from extensive computer experiments is compared with that of traditional microscopic and phenomenological models as well as with the performance of other learning systems, including earlier neural network models as well as the support vector machines recently applied to the same problem. In discussing the results, emphasis is placed on predictions for nuclei that are far from the stability line, and especially those involved in r-process nucleosynthesis. It is found that the new statistical models can match or even surpass the predictive performance of conventional models for β-decay systematics and accordingly should provide a valuable additional tool for exploring the expanding nuclear landscape.

Variability aware compact model characterization for statistical circuit design optimization

Science.gov (United States)

Qiao, Ying; Qian, Kun; Spanos, Costas J.

2012-03-01

Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose an efficient variabilityaware compact model characterization methodology based on the linear propagation of variance. Hierarchical spatial variability patterns of selected compact model parameters are directly calculated from transistor array test structures. This methodology has been implemented and tested using transistor I-V measurements and the EKV-EPFL compact model. Calculation results compare well to full-wafer direct model parameter extractions. Further studies are done on the proper selection of both compact model parameters and electrical measurement metrics used in the method.

SoS contract verification using statistical model checking

Directory of Open Access Journals (Sweden)

Alessandro Mignogna

2013-11-01

Full Text Available Exhaustive formal verification for systems of systems (SoS is impractical and cannot be applied on a large scale. In this paper we propose to use statistical model checking for efficient verification of SoS. We address three relevant aspects for systems of systems: 1 the model of the SoS, which includes stochastic aspects; 2 the formalization of the SoS requirements in the form of contracts; 3 the tool-chain to support statistical model checking for SoS. We adapt the SMC technique for application to heterogeneous SoS. We extend the UPDM/SysML specification language to express the SoS requirements that the implemented strategies over the SoS must satisfy. The requirements are specified with a new contract language specifically designed for SoS, targeting a high-level English- pattern language, but relying on an accurate semantics given by the standard temporal logics. The contracts are verified against the UPDM/SysML specification using the Statistical Model Checker (SMC PLASMA combined with the simulation engine DESYRE, which integrates heterogeneous behavioral models through the functional mock-up interface (FMI standard. The tool-chain allows computing an estimation of the satisfiability of the contracts by the SoS. The results help the system architect to trade-off different solutions to guide the evolution of the SoS.

Complex Data Modeling and Computationally Intensive Statistical Methods

CERN Document Server

Mantovan, Pietro

2010-01-01

The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets. The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statistici

Statistical methods for elimination of guarantee-time bias in cohort studies: a simulation study

Directory of Open Access Journals (Sweden)

In Sung Cho

2017-08-01

Full Text Available Abstract Background Aspirin has been considered to be beneficial in preventing cardiovascular diseases and cancer. Several pharmaco-epidemiology cohort studies have shown protective effects of aspirin on diseases using various statistical methods, with the Cox regression model being the most commonly used approach. However, there are some inherent limitations to the conventional Cox regression approach such as guarantee-time bias, resulting in an overestimation of the drug effect. To overcome such limitations, alternative approaches, such as the time-dependent Cox model and landmark methods have been proposed. This study aimed to compare the performance of three methods: Cox regression, time-dependent Cox model and landmark method with different landmark times in order to address the problem of guarantee-time bias. Methods Through statistical modeling and simulation studies, the performance of the above three methods were assessed in terms of type I error, bias, power, and mean squared error (MSE. In addition, the three statistical approaches were applied to a real data example from the Korean National Health Insurance Database. Effect of cumulative rosiglitazone dose on the risk of hepatocellular carcinoma was used as an example for illustration. Results In the simulated data, time-dependent Cox regression outperformed the landmark method in terms of bias and mean squared error but the type I error rates were similar. The results from real-data example showed the same patterns as the simulation findings. Conclusions While both time-dependent Cox regression model and landmark analysis are useful in resolving the problem of guarantee-time bias, time-dependent Cox regression is the most appropriate method for analyzing cumulative dose effects in pharmaco-epidemiological studies.

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

Science.gov (United States)

Müller, M. F.; Thompson, S. E.

2016-02-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

A statistical model for porous structure of rocks

Institute of Scientific and Technical Information of China (English)

JU Yang; YANG YongMing; SONG ZhenDuo; XU WenJing

2008-01-01

The geometric features and the distribution properties of pores in rocks were In-vestigated by means of CT scanning tests of sandstones. The centroidal coordl-nares of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob-ability density functions upon which the random distribution of pore position, dis-tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex-amine the stress distribution, the pattern of element failure and the inoaculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.

A statistical model for porous structure of rocks

Institute of Scientific and Technical Information of China (English)

2008-01-01

The geometric features and the distribution properties of pores in rocks were in- vestigated by means of CT scanning tests of sandstones. The centroidal coordi- nates of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob- ability density functions upon which the random distribution of pore position, dis- tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex- amine the stress distribution, the pattern of element failure and the inosculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.

Investigating Students' Acceptance of a Statistics Learning Platform Using Technology Acceptance Model

Science.gov (United States)

Song, Yanjie; Kong, Siu-Cheung

2017-01-01

The study aims at investigating university students' acceptance of a statistics learning platform to support the learning of statistics in a blended learning context. Three kinds of digital resources, which are simulations, online videos, and online quizzes, were provided on the platform. Premised on the technology acceptance model, we adopted a…

(ajst) statistical mechanics model for orientational

African Journals Online (AJOL)

Science and Engineering Series Vol. 6, No. 2, pp. 94 - 101. STATISTICAL MECHANICS MODEL FOR ORIENTATIONAL. MOTION OF TWO-DIMENSIONAL RIGID ROTATOR. Malo, J.O. ... there is no translational motion and that they are well separated so .... constant and I is the moment of inertia of a linear rotator. Thus, the ...

Performance modeling, stochastic networks, and statistical multiplexing

CERN Document Server

Mazumdar, Ravi R

2013-01-01

This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan

Bayesian Nonparametric Statistical Inference for Shock Models and Wear Processes.

Science.gov (United States)

1979-12-01

also note that the results in Section 2 do not depend on the support of F .) This shock model have been studied by Esary, Marshall and Proschan (1973...Barlow and Proschan (1975), among others. The analogy of the shock model in risk and acturial analysis has been given by BUhlmann (1970, Chapter 2... Mathematical Statistics, Vol. 4, pp. 894-906. Billingsley, P. (1968), CONVERGENCE OF PROBABILITY MEASURES, John Wiley, New York. BUhlmann, H. (1970

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

Science.gov (United States)

Thiessen, Erik D

2017-01-05

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik

Linear mixed models a practical guide using statistical software

CERN Document Server

West, Brady T; Galecki, Andrzej T

2014-01-01

Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM.New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggest...

Average Nuclear properties based on statistical model

International Nuclear Information System (INIS)

El-Jaick, L.J.

1974-01-01

The rough properties of nuclei were investigated by statistical model, in systems with the same and different number of protons and neutrons, separately, considering the Coulomb energy in the last system. Some average nuclear properties were calculated based on the energy density of nuclear matter, from Weizsscker-Beth mass semiempiric formulae, generalized for compressible nuclei. In the study of a s surface energy coefficient, the great influence exercised by Coulomb energy and nuclear compressibility was verified. For a good adjust of beta stability lines and mass excess, the surface symmetry energy were established. (M.C.K.) [pt

Acceleration transforms and statistical kinetic models

International Nuclear Information System (INIS)

LuValle, M.J.; Welsher, T.L.; Svoboda, K.

1988-01-01

For a restricted class of problems a mathematical model of microscopic degradation processes, statistical kinetics, is developed and linked through acceleration transforms to the information which can be obtained from a system in which the only observable sign of degradation is sudden and catastrophic failure. The acceleration transforms were developed in accelerated life testing applications as a tool for extrapolating from the observable results of an accelerated life test to the dynamics of the underlying degradation processes. A particular concern of a physicist attempting to interpreted the results of an analysis based on acceleration transforms is determining the physical species involved in the degradation process. These species may be (a) relatively abundant or (b) relatively rare. The main results of this paper are a theorem showing that for an important subclass of statistical kinetic models, acceleration transforms cannot be used to distinguish between cases a and b, and an example showing that in some cases falling outside the restrictions of the theorem, cases a and b can be distinguished by their acceleration transforms

Statistical models describing the energy signature of buildings

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Thavlov, Anders

2010-01-01

Approximately one third of the primary energy production in Denmark is used for heating in buildings. Therefore efforts to accurately describe and improve energy performance of the building mass are very important. For this purpose statistical models describing the energy signature of a building, i...... or varying energy prices. The paper will give an overview of statistical methods and applied models based on experiments carried out in FlexHouse, which is an experimental building in SYSLAB, Risø DTU. The models are of different complexity and can provide estimates of physical quantities such as UA......-values, time constants of the building, and other parameters related to the heat dynamics. A method for selecting the most appropriate model for a given building is outlined and finally a perspective of the applications is given. Aknowledgements to the Danish Energy Saving Trust and the Interreg IV ``Vind i...

STATISTICAL MODELS OF REPRESENTING INTELLECTUAL CAPITAL

Directory of Open Access Journals (Sweden)

Andreea Feraru

2016-06-01

Full Text Available This article entitled Statistical Models of Representing Intellectual Capital approaches and analyses the concept of intellectual capital, as well as the main models which can support enterprisers/managers in evaluating and quantifying the advantages of intellectual capital. Most authors examine intellectual capital from a static perspective and focus on the development of its various evaluation models. In this chapter we surveyed the classical static models: Sveiby, Edvisson, Balanced Scorecard, as well as the canonical model of intellectual capital. Among the group of static models for evaluating organisational intellectual capital the canonical model stands out. This model enables the structuring of organisational intellectual capital in: human capital, structural capital and relational capital. Although the model is widely spread, it is a static one and can thus create a series of errors in the process of evaluation, because all the three entities mentioned above are not independent from the viewpoint of their contents, as any logic of structuring complex entities requires.

Performance of the S - [chi][squared] Statistic for Full-Information Bifactor Models

Science.gov (United States)

Li, Ying; Rupp, Andre A.

2011-01-01

This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…

In vitro radiosensitivity of six human cell lines. A comparative study with different statistical models

International Nuclear Information System (INIS)

Fertil, B.; Deschavanne, P.J.; Lachet, B.; Malaise, E.P.

1980-01-01

The intrinsic radiosensitivity of human cell lines (five tumor and one nontransformed fibroblastic) was studied in vitro. The survival curves were fitted by the single-hit multitarget, the two-hit multitarget, the single-hit multitarget with initial slope, and the quadratic models. The accuracy of the experimental results permitted evaluation of the various fittings. Both a statistical test (comparison of variances left unexplained by the four models) and a biological consideration (check for independence of the fitted parameters vis-a-vis the portion of the survival curve in question) were carried out. The quadratic model came out best with each of them. It described the low-dose effects satisfactorily, revealing a single-hit lethal component. This finding and the fact that the six survival curves displayed a continuous curvature ruled out the adoption of the target models as well as the widely used linear regression. As calculated by the quadratic model, the parameters of the six cell lines lead to the following conclusions: (a) the intrinsic radiosensitivity varies greatly among the different cell lines; (b) the interpretation of the fibroblast survival curve is not basically different from that of the tumor cell lines; and (c) the radiosensitivity of these human cell lines is comparable to that of other mammalian cell lines

The epistemology of mathematical and statistical modeling: a quiet methodological revolution.

Science.gov (United States)

Rodgers, Joseph Lee

2010-01-01

A quiet methodological revolution, a modeling revolution, has occurred over the past several decades, almost without discussion. In contrast, the 20th century ended with contentious argument over the utility of null hypothesis significance testing (NHST). The NHST controversy may have been at least partially irrelevant, because in certain ways the modeling revolution obviated the NHST argument. I begin with a history of NHST and modeling and their relation to one another. Next, I define and illustrate principles involved in developing and evaluating mathematical models. Following, I discuss the difference between using statistical procedures within a rule-based framework and building mathematical models from a scientific epistemology. Only the former is treated carefully in most psychology graduate training. The pedagogical implications of this imbalance and the revised pedagogy required to account for the modeling revolution are described. To conclude, I discuss how attention to modeling implies shifting statistical practice in certain progressive ways. The epistemological basis of statistics has moved away from being a set of procedures, applied mechanistically, and moved toward building and evaluating statistical and scientific models. Copyrigiht 2009 APA, all rights reserved.

Establishing statistical models of manufacturing parameters

International Nuclear Information System (INIS)

Senevat, J.; Pape, J.L.; Deshayes, J.F.

1991-01-01

This paper reports on the effect of pilgering and cold-work parameters on contractile strain ratio and mechanical properties that were investigated using a large population of Zircaloy tubes. Statistical models were established between: contractile strain ratio and tooling parameters, mechanical properties (tensile test, creep test) and cold-work parameters, and mechanical properties and stress-relieving temperature

Statistical modeling of nitrogen-dependent modulation of root system architecture in Arabidopsis thaliana.

Science.gov (United States)

Araya, Takao; Kubo, Takuya; von Wirén, Nicolaus; Takahashi, Hideki

2016-03-01

Plant root development is strongly affected by nutrient availability. Despite the importance of structure and function of roots in nutrient acquisition, statistical modeling approaches to evaluate dynamic and temporal modulations of root system architecture in response to nutrient availability have remained as widely open and exploratory areas in root biology. In this study, we developed a statistical modeling approach to investigate modulations of root system architecture in response to nitrogen availability. Mathematical models were designed for quantitative assessment of root growth and root branching phenotypes and their dynamic relationships based on hierarchical configuration of primary and lateral roots formulating the fishbone-shaped root system architecture in Arabidopsis thaliana. Time-series datasets reporting dynamic changes in root developmental traits on different nitrate or ammonium concentrations were generated for statistical analyses. Regression analyses unraveled key parameters associated with: (i) inhibition of primary root growth under nitrogen limitation or on ammonium; (ii) rapid progression of lateral root emergence in response to ammonium; and (iii) inhibition of lateral root elongation in the presence of excess nitrate or ammonium. This study provides a statistical framework for interpreting dynamic modulation of root system architecture, supported by meta-analysis of datasets displaying morphological responses of roots to diverse nitrogen supplies. © 2015 Institute of Botany, Chinese Academy of Sciences.

Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies

Directory of Open Access Journals (Sweden)

Katy Denise Heath

2014-04-01

Full Text Available Predicting how species interactions evolve requires that we understand the mechanistic basis of coevolution, and thus the functional genotype-by-genotype interactions (G × G that drive reciprocal natural selection. Theory on host-parasite coevolution provides testable hypotheses for empiricists, but depends upon models of functional G × G that remain loosely tethered to the molecular details of any particular system. In practice, reciprocal cross-infection studies are often used to partition the variation in infection or fitness in a population that is attributable to G × G (statistical G × G. Here we use simulations to demonstrate that within-population statistical G × G likely tells us little about the existence of coevolution, its strength, or the genetic basis of functional G × G. Combined with studies of multiple populations or points in time, mapping and molecular techniques can bridge the gap between natural variation and mechanistic models of coevolution, while model-based statistics can formally confront coevolutionary models with cross-infection data. Together these approaches provide a robust framework for inferring the infection genetics underlying statistical G × G, helping unravel the genetic basis of coevolution.

Statistical geological discrete fracture network model. Forsmark modelling stage 2.2

Energy Technology Data Exchange (ETDEWEB)

Fox, Aaron; La Pointe, Paul [Golder Associates Inc (United States); Simeonov, Assen [Swedish Nuclear Fuel and Waste Management Co., Stockholm (Sweden); Hermanson, Jan; Oehman, Johan [Golder Associates AB, Stockholm (Sweden)

2007-11-15

The Swedish Nuclear Fuel and Waste Management Company (SKB) is performing site characterization at two different locations, Forsmark and Laxemar, in order to locate a site for a final geologic repository for spent nuclear fuel. The program is built upon the development of Site Descriptive Models (SDMs) at specific timed data freezes. Each SDM is formed from discipline-specific reports from across the scientific spectrum. This report describes the methods, analyses, and conclusions of the geological modeling team with respect to a geological and statistical model of fractures and minor deformation zones (henceforth referred to as the geological DFN), version 2.2, at the Forsmark site. The geological DFN builds upon the work of other geological modelers, including the deformation zone (DZ), rock domain (RD), and fracture domain (FD) models. The geological DFN is a statistical model for stochastically simulating rock fractures and minor deformation zones as a scale of less than 1,000 m (the lower cut-off of the DZ models). The geological DFN is valid within four specific fracture domains inside the local model region, and encompassing the candidate volume at Forsmark: FFM01, FFM02, FFM03, and FFM06. The models are build using data from detailed surface outcrop maps and the cored borehole record at Forsmark. The conceptual model for the Forsmark 2.2 geological revolves around the concept of orientation sets; for each fracture domain, other model parameters such as size and intensity are tied to the orientation sets. Two classes of orientation sets were described; Global sets, which are encountered everywhere in the model region, and Local sets, which represent highly localized stress environments. Orientation sets were described in terms of their general cardinal direction (NE, NW, etc). Two alternatives are presented for fracture size modeling: - the tectonic continuum approach (TCM, TCMF) described by coupled size-intensity scaling following power law distributions

Statistical geological discrete fracture network model. Forsmark modelling stage 2.2

International Nuclear Information System (INIS)

Fox, Aaron; La Pointe, Paul; Simeonov, Assen; Hermanson, Jan; Oehman, Johan

2007-11-01

The Swedish Nuclear Fuel and Waste Management Company (SKB) is performing site characterization at two different locations, Forsmark and Laxemar, in order to locate a site for a final geologic repository for spent nuclear fuel. The program is built upon the development of Site Descriptive Models (SDMs) at specific timed data freezes. Each SDM is formed from discipline-specific reports from across the scientific spectrum. This report describes the methods, analyses, and conclusions of the geological modeling team with respect to a geological and statistical model of fractures and minor deformation zones (henceforth referred to as the geological DFN), version 2.2, at the Forsmark site. The geological DFN builds upon the work of other geological modelers, including the deformation zone (DZ), rock domain (RD), and fracture domain (FD) models. The geological DFN is a statistical model for stochastically simulating rock fractures and minor deformation zones as a scale of less than 1,000 m (the lower cut-off of the DZ models). The geological DFN is valid within four specific fracture domains inside the local model region, and encompassing the candidate volume at Forsmark: FFM01, FFM02, FFM03, and FFM06. The models are build using data from detailed surface outcrop maps and the cored borehole record at Forsmark. The conceptual model for the Forsmark 2.2 geological revolves around the concept of orientation sets; for each fracture domain, other model parameters such as size and intensity are tied to the orientation sets. Two classes of orientation sets were described; Global sets, which are encountered everywhere in the model region, and Local sets, which represent highly localized stress environments. Orientation sets were described in terms of their general cardinal direction (NE, NW, etc). Two alternatives are presented for fracture size modeling: - the tectonic continuum approach (TCM, TCMF) described by coupled size-intensity scaling following power law distributions

Using statistical models to explore ensemble uncertainty in climate impact studies: the example of air pollution in Europe

Directory of Open Access Journals (Sweden)

V. E. P. Lemaire

2016-03-01

Full Text Available Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projections. However, the computing cost of such methods requires optimizing ensemble exploration techniques. By using a training data set from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for eight regions in Europe and developed statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows selecting the members of the EuroCordex ensemble of regional climate projections that should be used in priority for future air quality projections (CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM following the EuroCordex terminology. After having tested the validity of the statistical model in predictive mode, we can provide ranges of uncertainty attributed to the spread of the regional climate projection ensemble by the end of the century (2071–2100 for the RCP8.5. In the three regions where the statistical model of the impact of climate change on PM2.5 offers satisfactory performances, we find a climate benefit (a decrease of PM2.5 concentrations under future climate of −1.08 (±0.21, −1.03 (±0.32, −0.83 (±0.14 µg m−3, for respectively Eastern Europe, Mid-Europe and Northern Italy. In the British-Irish Isles, Scandinavia, France, the Iberian Peninsula and the Mediterranean, the statistical model is not considered skillful enough to draw any conclusion for PM2.5. In Eastern Europe, France, the Iberian Peninsula, Mid-Europe and Northern Italy, the statistical model of the

Statistical Modelling of the Soil Dielectric Constant

Science.gov (United States)

Usowicz, Boguslaw; Marczewski, Wojciech; Bogdan Usowicz, Jerzy; Lipiec, Jerzy

2010-05-01

The dielectric constant of soil is the physical property being very sensitive on water content. It funds several electrical measurement techniques for determining the water content by means of direct (TDR, FDR, and others related to effects of electrical conductance and/or capacitance) and indirect RS (Remote Sensing) methods. The work is devoted to a particular statistical manner of modelling the dielectric constant as the property accounting a wide range of specific soil composition, porosity, and mass density, within the unsaturated water content. Usually, similar models are determined for few particular soil types, and changing the soil type one needs switching the model on another type or to adjust it by parametrization of soil compounds. Therefore, it is difficult comparing and referring results between models. The presented model was developed for a generic representation of soil being a hypothetical mixture of spheres, each representing a soil fraction, in its proper phase state. The model generates a serial-parallel mesh of conductive and capacitive paths, which is analysed for a total conductive or capacitive property. The model was firstly developed to determine the thermal conductivity property, and now it is extended on the dielectric constant by analysing the capacitive mesh. The analysis is provided by statistical means obeying physical laws related to the serial-parallel branching of the representative electrical mesh. Physical relevance of the analysis is established electrically, but the definition of the electrical mesh is controlled statistically by parametrization of compound fractions, by determining the number of representative spheres per unitary volume per fraction, and by determining the number of fractions. That way the model is capable covering properties of nearly all possible soil types, all phase states within recognition of the Lorenz and Knudsen conditions. In effect the model allows on generating a hypothetical representative of

Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

Science.gov (United States)

Qi, D.; Majda, A.

2017-12-01

A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with

Bayesian models based on test statistics for multiple hypothesis testing problems.

Science.gov (United States)

Ji, Yuan; Lu, Yiling; Mills, Gordon B

2008-04-01

We propose a Bayesian method for the problem of multiple hypothesis testing that is routinely encountered in bioinformatics research, such as the differential gene expression analysis. Our algorithm is based on modeling the distributions of test statistics under both null and alternative hypotheses. We substantially reduce the complexity of the process of defining posterior model probabilities by modeling the test statistics directly instead of modeling the full data. Computationally, we apply a Bayesian FDR approach to control the number of rejections of null hypotheses. To check if our model assumptions for the test statistics are valid for various bioinformatics experiments, we also propose a simple graphical model-assessment tool. Using extensive simulations, we demonstrate the performance of our models and the utility of the model-assessment tool. In the end, we apply the proposed methodology to an siRNA screening and a gene expression experiment.

On-the-fly confluence detection for statistical model checking (extended version)

NARCIS (Netherlands)

Hartmanns, Arnd; Timmer, Mark

Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the

An efficient approach to transient turbulent dispersion modeling by CFD-statistical analysis of a many-puff system

International Nuclear Information System (INIS)

Ching, W-H; K H Leung, Michael; Leung, Dennis Y C

2009-01-01

Transient turbulent dispersion phenomena can be found in various practical problems, such as the accidental release of toxic chemical vapor and the airborne transmission of infectious droplets. Computational fluid dynamics (CFD) is an effective tool for analyzing such transient dispersion behaviors. However, the transient CFD analysis is often computationally expensive and time consuming. In the present study, a computationally efficient CFD-statistical hybrid modeling method has been developed for studying transient turbulent dispersion. In this method, the source emission is represented by emissions of many infinitesimal puffs. Statistical analysis is performed to obtain first the statistical properties of the puff trajectories and subsequently the most probable distribution of the puff trajectories that represent the macroscopic dispersion behaviors. In two case studies of ambient dispersion, the numerical modeling results obtained agree reasonably well with both experimental measurements and conventional k-ε modeling results published in the literature. More importantly, the proposed many-puff CFD-statistical hybrid modeling method effectively reduces the computational time by two orders of magnitude.

Growth Curve and Structural Equation Modeling : Topics from the Indian Statistical Institute

CERN Document Server

2015-01-01

This book describes some recent trends in GCM research on different subject areas, both theoretical and applied. This includes tools and possibilities for further work through new techniques and modification of existing ones. A growth curve is an empirical model of the evolution of a quantity over time. Growth curves in longitudinal studies are used in disciplines including biology, statistics, population studies, economics, biological sciences, sociology, nano-biotechnology, and fluid mechanics. The volume includes original studies, theoretical findings and case studies from a wide range of applied work. This volume builds on presentations from a GCM workshop held at the Indian Statistical Institute, Giridih, January 18-19, 2014. This book follows the volume Advances in Growth Curve Models, published by Springer in 2013. The results have meaningful application in health care, prediction of crop yield, child nutrition, poverty measurements, estimation of growth rate, and other research areas.

A comparative study of two statistical approaches for the analysis of real seismicity sequences and synthetic seismicity generated by a stick-slip experimental model

Science.gov (United States)

Flores-Marquez, Leticia Elsa; Ramirez Rojaz, Alejandro; Telesca, Luciano

2015-04-01

The study of two statistical approaches is analyzed for two different types of data sets, one is the seismicity generated by the subduction processes occurred at south Pacific coast of Mexico between 2005 and 2012, and the other corresponds to the synthetic seismic data generated by a stick-slip experimental model. The statistical methods used for the present study are the visibility graph in order to investigate the time dynamics of the series and the scaled probability density function in the natural time domain to investigate the critical order of the system. This comparison has the purpose to show the similarities between the dynamical behaviors of both types of data sets, from the point of view of critical systems. The observed behaviors allow us to conclude that the experimental set up globally reproduces the behavior observed in the statistical approaches used to analyses the seismicity of the subduction zone. The present study was supported by the Bilateral Project Italy-Mexico Experimental Stick-slip models of tectonic faults: innovative statistical approaches applied to synthetic seismic sequences, jointly funded by MAECI (Italy) and AMEXCID (Mexico) in the framework of the Bilateral Agreement for Scientific and Technological Cooperation PE 2014-2016.

Strategies for Testing Statistical and Practical Significance in Detecting DIF with Logistic Regression Models

Science.gov (United States)

Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza

2014-01-01

This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…

Analytical model of SiPM time resolution and order statistics with crosstalk

International Nuclear Information System (INIS)

Vinogradov, S.

2015-01-01

Time resolution is the most important parameter of photon detectors in a wide range of time-of-flight and time correlation applications within the areas of high energy physics, medical imaging, and others. Silicon photomultipliers (SiPM) have been initially recognized as perfect photon-number-resolving detectors; now they also provide outstanding results in the scintillator timing resolution. However, crosstalk and afterpulsing introduce false secondary non-Poissonian events, and SiPM time resolution models are experiencing significant difficulties with that. This study presents an attempt to develop an analytical model of the timing resolution of an SiPM taking into account statistics of secondary events resulting from a crosstalk. Two approaches have been utilized to derive an analytical expression for time resolution: the first one based on statistics of independent identically distributed detection event times and the second one based on order statistics of these times. The first approach is found to be more straightforward and “analytical-friendly” to model analog SiPMs. Comparisons of coincidence resolving times predicted by the model with the known experimental results from a LYSO:Ce scintillator and a Hamamatsu MPPC are presented

Analytical model of SiPM time resolution and order statistics with crosstalk

Energy Technology Data Exchange (ETDEWEB)

Vinogradov, S., E-mail: Sergey.Vinogradov@liverpool.ac.uk [University of Liverpool and Cockcroft Institute, Sci-Tech Daresbury, Keckwick Lane, Warrington WA4 4AD (United Kingdom); P.N. Lebedev Physical Institute of the Russian Academy of Sciences, 119991 Leninskiy Prospekt 53, Moscow (Russian Federation)

2015-07-01

Time resolution is the most important parameter of photon detectors in a wide range of time-of-flight and time correlation applications within the areas of high energy physics, medical imaging, and others. Silicon photomultipliers (SiPM) have been initially recognized as perfect photon-number-resolving detectors; now they also provide outstanding results in the scintillator timing resolution. However, crosstalk and afterpulsing introduce false secondary non-Poissonian events, and SiPM time resolution models are experiencing significant difficulties with that. This study presents an attempt to develop an analytical model of the timing resolution of an SiPM taking into account statistics of secondary events resulting from a crosstalk. Two approaches have been utilized to derive an analytical expression for time resolution: the first one based on statistics of independent identically distributed detection event times and the second one based on order statistics of these times. The first approach is found to be more straightforward and “analytical-friendly” to model analog SiPMs. Comparisons of coincidence resolving times predicted by the model with the known experimental results from a LYSO:Ce scintillator and a Hamamatsu MPPC are presented.

UPPAAL-SMC: Statistical Model Checking for Priced Timed Automata

DEFF Research Database (Denmark)

Bulychev, Petr; David, Alexandre; Larsen, Kim Guldstrand

2012-01-01

on a series of extensions of the statistical model checking approach generalized to handle real-time systems and estimate undecidable problems. U PPAAL - SMC comes together with a friendly user interface that allows a user to specify complex problems in an efficient manner as well as to get feedback...... in the form of probability distributions and compare probabilities to analyze performance aspects of systems. The focus of the survey is on the evolution of the tool – including modeling and specification formalisms as well as techniques applied – together with applications of the tool to case studies....

Topology for Statistical Modeling of Petascale Data

Energy Technology Data Exchange (ETDEWEB)

Bennett, Janine Camille [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pebay, Philippe Pierre [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Rojas, Maurice [Texas A & M Univ., College Station, TX (United States)

2014-07-01

This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled "Topology for Statistical Modeling of Petascale Data", funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program.

A Formal Approach for RT-DVS Algorithms Evaluation Based on Statistical Model Checking

Directory of Open Access Journals (Sweden)

Shengxin Dai

2015-01-01

Full Text Available Energy saving is a crucial concern in embedded real time systems. Many RT-DVS algorithms have been proposed to save energy while preserving deadline guarantees. This paper presents a novel approach to evaluate RT-DVS algorithms using statistical model checking. A scalable framework is proposed for RT-DVS algorithms evaluation, in which the relevant components are modeled as stochastic timed automata, and the evaluation metrics including utilization bound, energy efficiency, battery awareness, and temperature awareness are expressed as statistical queries. Evaluation of these metrics is performed by verifying the corresponding queries using UPPAAL-SMC and analyzing the statistical information provided by the tool. We demonstrate the applicability of our framework via a case study of five classical RT-DVS algorithms.

Filtering a statistically exactly solvable test model for turbulent tracers from partial observations

International Nuclear Information System (INIS)

Gershgorin, B.; Majda, A.J.

2011-01-01

A statistically exactly solvable model for passive tracers is introduced as a test model for the authors' Nonlinear Extended Kalman Filter (NEKF) as well as other filtering algorithms. The model involves a Gaussian velocity field and a passive tracer governed by the advection-diffusion equation with an imposed mean gradient. The model has direct relevance to engineering problems such as the spread of pollutants in the air or contaminants in the water as well as climate change problems concerning the transport of greenhouse gases such as carbon dioxide with strongly intermittent probability distributions consistent with the actual observations of the atmosphere. One of the attractive properties of the model is the existence of the exact statistical solution. In particular, this unique feature of the model provides an opportunity to design and test fast and efficient algorithms for real-time data assimilation based on rigorous mathematical theory for a turbulence model problem with many active spatiotemporal scales. Here, we extensively study the performance of the NEKF which uses the exact first and second order nonlinear statistics without any approximations due to linearization. The role of partial and sparse observations, the frequency of observations and the observation noise strength in recovering the true signal, its spectrum, and fat tail probability distribution are the central issues discussed here. The results of our study provide useful guidelines for filtering realistic turbulent systems with passive tracers through partial observations.

A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity.

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Zhu, Yingying; Zhu, Xiaofeng; Kim, Minjeong; Yan, Jin; Wu, Guorong

2017-06-01

Functional connectivity (FC) has been widely investigated in many imaging-based neuroscience and clinical studies. Since functional Magnetic Resonance Image (MRI) signal is just an indirect reflection of brain activity, it is difficult to accurately quantify the FC strength only based on signal correlation. To address this limitation, we propose a learning-based tensor model to derive high sensitivity and specificity connectome biomarkers at the individual level from resting-state fMRI images. First, we propose a learning-based approach to estimate the intrinsic functional connectivity. In addition to the low level region-to-region signal correlation, latent module-to-module connection is also estimated and used to provide high level heuristics for measuring connectivity strength. Furthermore, sparsity constraint is employed to automatically remove the spurious connections, thus alleviating the issue of searching for optimal threshold. Second, we integrate our learning-based approach with the sliding-window technique to further reveal the dynamics of functional connectivity. Specifically, we stack the functional connectivity matrix within each sliding window and form a 3D tensor where the third dimension denotes for time. Then we obtain dynamic functional connectivity (dFC) for each individual subject by simultaneously estimating the within-sliding-window functional connectivity and characterizing the across-sliding-window temporal dynamics. Third, in order to enhance the robustness of the connectome patterns extracted from dFC, we extend the individual-based 3D tensors to a population-based 4D tensor (with the fourth dimension stands for the training subjects) and learn the statistics of connectome patterns via 4D tensor analysis. Since our 4D tensor model jointly (1) optimizes dFC for each training subject and (2) captures the principle connectome patterns, our statistical model gains more statistical power of representing new subject than current state

Physics-based statistical model and simulation method of RF propagation in urban environments

Science.gov (United States)

Pao, Hsueh-Yuan; Dvorak, Steven L.

2010-09-14

A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.

Methodological Problems Of Statistical Study Of Regional Tourism And Tourist Expenditure

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Anton Olegovich Ovcharov

2015-03-01

Full Text Available The aim of the work is the analysis of the problems of regional tourism statistics. The subject of the research is the tourism expenditure, the specificity of their recording and modeling. The methods of statistical observation and factor analysis are used. The article shows the features and directions of statistical methodology of tourism. A brief review of international publications on statistical studies of tourist expenditure is made. It summarizes the data from different statistical forms and shows the positive and negative trends in the development of tourism in Russia. It is concluded that the tourist industry in Russia is focused on outbound tourism rather than on inbound or internal. The features of statistical accounting and statistical analysis of tourism expenditure in Russian and international statistics are described. To assess the level of development of regional tourism the necessity of use the coefficient of efficiency of tourism. The reasons of the prevalence of imports over exports of tourism services are revealed using the data of the balance of payments. This is due to the raw material orientation of Russian exports and low specific weight of the account “Services” in the structure of the balance of payments. The additive model is also proposed in the paper. It describes the influence of three factors on the changes in tourist expenditure. These factors are the number of trips, the cost of a trip and structural changes in destinations and travel purposes. On the basis of the data from 2012–2013 we estimate the force and the direction of the influence of each factor. Testing of the model showed that the increase in tourism exports caused by the combined positive impact of all three factors, chief of which is the growing number of foreigners who visited Russia during the concerned period.

Statistical study of clone survival curves after irradiation in one or two stages. Comparison and generalization of different models

International Nuclear Information System (INIS)

Lachet, Bernard.

1975-01-01

A statistical study was carried out on 208 survival curves for chlorella subjected to γ or particle radiations. The computing programmes used were written in Fortran. The different experimental causes contributing to the variance of a survival rate are analyzed and consequently the experiments can be planned. Each curve was fitted to four models by the weighted least squares method applied to non-linear functions. The validity of the fits obtained can be checked by the F test. It was possible to define the confidence and prediction zones around an adjusted curve by weighting of the residual variance, in spite of error on the doses delivered; the confidence limits can them be fixed for a dose estimated from an exact or measured survival. The four models adopted were compared for the precision of their fit (by a non-parametric simultaneous comparison test) and the scattering of their adjusted parameters: Wideroe's model gives a very good fit with the experimental points in return for a scattering of its parameters, which robs them of their presumed meaning. The principal component analysis showed the statistical equivalence of the 1 and 2 hit target models. Division of the irradiation into two doses, the first fixed by the investigator, leads to families of curves for which the equation was established from that of any basic model expressing the dose survival relationship in one-stage irradiation [fr

Encoding Dissimilarity Data for Statistical Model Building.

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Wahba, Grace

2010-12-01

We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into a Euclidean space which respects the dissimilarity information while controlling the dimension of the space. A "newbie" algorithm is provided for embedding new objects into this space. This allows the dissimilarity information to be incorporated into a Smoothing Spline ANOVA penalized likelihood model, a Support Vector Machine, or any model that will admit Reproducing Kernel Hilbert Space components, for nonparametric regression, supervised learning, or semi-supervised learning. Future work and open questions are discussed. The papers are: F. Lu, S. Keles, S. Wright and G. Wahba 2005. A framework for kernel regularization with application to protein clustering. Proceedings of the National Academy of Sciences 102, 12332-1233.G. Corrada Bravo, G. Wahba, K. Lee, B. Klein, R. Klein and S. Iyengar 2009. Examining the relative influence of familial, genetic and environmental covariate information in flexible risk models. Proceedings of the National Academy of Sciences 106, 8128-8133F. Lu, Y. Lin and G. Wahba. Robust manifold unfolding with kernel regularization. TR 1008, Department of Statistics, University of Wisconsin-Madison.

Determination of daily solar ultraviolet radiation using statistical models and artificial neural networks

Directory of Open Access Journals (Sweden)

F. J. Barbero

2006-09-01

Full Text Available In this study, two different methodologies are used to develop two models for estimating daily solar UV radiation. The first is based on traditional statistical techniques whereas the second is based on artificial neural network methods. Both models use daily solar global broadband radiation as the only measured input. The statistical model is derived from a relationship between the daily UV and the global clearness indices but modulated by the relative optical air mass. The inputs to the neural network model were determined from a large number of radiometric and atmospheric parameters using the automatic relevance determination method, although only the daily solar global irradiation, daily global clearness index and relative optical air mass were shown to be the optimal input variables. Both statistical and neural network models were developed using data measured at Almería (Spain, a semiarid and coastal climate, and tested against data from Table Mountain (Golden, CO, USA, a mountainous and dry environment. Results show that the statistical model performs adequately in both sites for all weather conditions, especially when only snow-free days at Golden were considered (RMSE=4.6%, MBE= –0.1%. The neural network based model provides the best overall estimates in the site where it has been trained, but presents an inadequate performance for the Golden site when snow-covered days are included (RMSE=6.5%, MBE= –3.0%. This result confirms that the neural network model does not adequately respond on those ranges of the input parameters which were not used for its development.

Simple classical model for Fano statistics in radiation detectors

Energy Technology Data Exchange (ETDEWEB)

Jordan, David V. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)], E-mail: David.Jordan@pnl.gov; Renholds, Andrea S.; Jaffe, John E.; Anderson, Kevin K.; Rene Corrales, L.; Peurrung, Anthony J. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)

2008-02-01

A simple classical model that captures the essential statistics of energy partitioning processes involved in the creation of information carriers (ICs) in radiation detectors is presented. The model pictures IC formation from a fixed amount of deposited energy in terms of the statistically analogous process of successively sampling water from a large, finite-volume container ('bathtub') with a small dipping implement ('shot or whiskey glass'). The model exhibits sub-Poisson variance in the distribution of the number of ICs generated (the 'Fano effect'). Elementary statistical analysis of the model clarifies the role of energy conservation in producing the Fano effect and yields Fano's prescription for computing the relative variance of the IC number distribution in terms of the mean and variance of the underlying, single-IC energy distribution. The partitioning model is applied to the development of the impact ionization cascade in semiconductor radiation detectors. It is shown that, in tandem with simple assumptions regarding the distribution of energies required to create an (electron, hole) pair, the model yields an energy-independent Fano factor of 0.083, in accord with the lower end of the range of literature values reported for silicon and high-purity germanium. The utility of this simple picture as a diagnostic tool for guiding or constraining more detailed, 'microscopic' physical models of detector material response to ionizing radiation is discussed.

Statistical properties of compartmental model parameters extracted from dynamic positron emission tomography experiments

International Nuclear Information System (INIS)

Mazoyer, B.M.; Huesman, R.H.; Budinger, T.F.; Knittel, B.L.

1986-01-01

Over the past years a major focus of research in physiologic studies employing tracers has been the computer implementation of mathematical methods of kinetic modeling for extracting the desired physiological parameters from tomographically derived data. A study is reported of factors that affect the statistical properties of compartmental model parameters extracted from dynamic positron emission tomography (PET) experiments

A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.

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Jacobo Pardo-Seco

Full Text Available BACKGROUND: Mitochondrial DNA (mtDNA variation (i.e. haplogroups has been analyzed in regards to a number of multifactorial diseases. The statistical power of a case-control study determines the a priori probability to reject the null hypothesis of homogeneity between cases and controls. METHODS/PRINCIPAL FINDINGS: We critically review previous approaches to the estimation of the statistical power based on the restricted scenario where the number of cases equals the number of controls, and propose a methodology that broadens procedures to more general situations. We developed statistical procedures that consider different disease scenarios, variable sample sizes in cases and controls, and variable number of haplogroups and effect sizes. The results indicate that the statistical power of a particular study can improve substantially by increasing the number of controls with respect to cases. In the opposite direction, the power decreases substantially when testing a growing number of haplogroups. We developed mitPower (http://bioinformatics.cesga.es/mitpower/, a web-based interface that implements the new statistical procedures and allows for the computation of the a priori statistical power in variable scenarios of case-control study designs, or e.g. the number of controls needed to reach fixed effect sizes. CONCLUSIONS/SIGNIFICANCE: The present study provides with statistical procedures for the computation of statistical power in common as well as complex case-control study designs involving 2×k tables, with special application (but not exclusive to mtDNA studies. In order to reach a wide range of researchers, we also provide a friendly web-based tool--mitPower--that can be used in both retrospective and prospective case-control disease studies.

Statistical learning modeling method for space debris photometric measurement

Science.gov (United States)

Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen

2016-03-01

Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science

Science.gov (United States)

Caron, L.; Ivins, E. R.; Larour, E.; Adhikari, S.; Nilsson, J.; Blewitt, G.

2018-03-01

We provide a new analysis of glacial isostatic adjustment (GIA) with the goal of assembling the model uncertainty statistics required for rigorously extracting trends in surface mass from the Gravity Recovery and Climate Experiment (GRACE) mission. Such statistics are essential for deciphering sea level, ocean mass, and hydrological changes because the latter signals can be relatively small (≤2 mm/yr water height equivalent) over very large regions, such as major ocean basins and watersheds. With abundant new >7 year continuous measurements of vertical land motion (VLM) reported by Global Positioning System stations on bedrock and new relative sea level records, our new statistical evaluation of GIA uncertainties incorporates Bayesian methodologies. A unique aspect of the method is that both the ice history and 1-D Earth structure vary through a total of 128,000 forward models. We find that best fit models poorly capture the statistical inferences needed to correctly invert for lower mantle viscosity and that GIA uncertainty exceeds the uncertainty ascribed to trends from 14 years of GRACE data in polar regions.

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

Science.gov (United States)

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable

Subject-enabled analytics model on measurement statistics in health risk expert system for public health informatics.

Science.gov (United States)

Chung, Chi-Jung; Kuo, Yu-Chen; Hsieh, Yun-Yu; Li, Tsai-Chung; Lin, Cheng-Chieh; Liang, Wen-Miin; Liao, Li-Na; Li, Chia-Ing; Lin, Hsueh-Chun

2017-11-01

This study applied open source technology to establish a subject-enabled analytics model that can enhance measurement statistics of case studies with the public health data in cloud computing. The infrastructure of the proposed model comprises three domains: 1) the health measurement data warehouse (HMDW) for the case study repository, 2) the self-developed modules of online health risk information statistics (HRIStat) for cloud computing, and 3) the prototype of a Web-based process automation system in statistics (PASIS) for the health risk assessment of case studies with subject-enabled evaluation. The system design employed freeware including Java applications, MySQL, and R packages to drive a health risk expert system (HRES). In the design, the HRIStat modules enforce the typical analytics methods for biomedical statistics, and the PASIS interfaces enable process automation of the HRES for cloud computing. The Web-based model supports both modes, step-by-step analysis and auto-computing process, respectively for preliminary evaluation and real time computation. The proposed model was evaluated by computing prior researches in relation to the epidemiological measurement of diseases that were caused by either heavy metal exposures in the environment or clinical complications in hospital. The simulation validity was approved by the commercial statistics software. The model was installed in a stand-alone computer and in a cloud-server workstation to verify computing performance for a data amount of more than 230K sets. Both setups reached efficiency of about 10 5 sets per second. The Web-based PASIS interface can be used for cloud computing, and the HRIStat module can be flexibly expanded with advanced subjects for measurement statistics. The analytics procedure of the HRES prototype is capable of providing assessment criteria prior to estimating the potential risk to public health. Copyright © 2017 Elsevier B.V. All rights reserved.

A model independent safeguard against background mismodeling for statistical inference

Energy Technology Data Exchange (ETDEWEB)

Priel, Nadav; Landsman, Hagar; Manfredini, Alessandro; Budnik, Ranny [Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Herzl St. 234, Rehovot (Israel); Rauch, Ludwig, E-mail: nadav.priel@weizmann.ac.il, E-mail: rauch@mpi-hd.mpg.de, E-mail: hagar.landsman@weizmann.ac.il, E-mail: alessandro.manfredini@weizmann.ac.il, E-mail: ran.budnik@weizmann.ac.il [Teilchen- und Astroteilchenphysik, Max-Planck-Institut für Kernphysik, Saupfercheckweg 1, 69117 Heidelberg (Germany)

2017-05-01

We propose a safeguard procedure for statistical inference that provides universal protection against mismodeling of the background. The method quantifies and incorporates the signal-like residuals of the background model into the likelihood function, using information available in a calibration dataset. This prevents possible false discovery claims that may arise through unknown mismodeling, and corrects the bias in limit setting created by overestimated or underestimated background. We demonstrate how the method removes the bias created by an incomplete background model using three realistic case studies.

Statistics Graduate Students' Professional Development for Teaching: A Communities of Practice Model

Science.gov (United States)

Justice, Nicola

Graduate teaching assistants (GTAs) are responsible for instructing approximately 25% of introductory statistics courses in the United States (Blair, Kirkman, & Maxwell, 2013). Most research on GTA professional development focuses on structured activities (e.g., courses, workshops) that have been developed to improve GTAs' pedagogy and content knowledge. Few studies take into account the social contexts of GTAs' professional development. However, GTAs perceive their social interactions with other GTAs to be a vital part of their preparation and support for teaching (e.g., Staton & Darling, 1989). Communities of practice (CoPs) are one way to bring together the study of the social contexts and structured activities of GTA professional development. CoPs are defined as groups of practitioners who deepen their knowledge and expertise by interacting with each other on an ongoing basis (e.g., Lave & Wenger, 1991). Graduate students may participate in CoPs related to teaching in many ways, including attending courses or workshops, participating in weekly meetings, engaging in informal discussions about teaching, or participating in e-mail conversations related to teaching tasks. This study explored the relationship between statistics graduate students' experiences in CoPs and the extent to which they hold student-centered teaching beliefs. A framework for characterizing GTAs' experiences in CoPs was described and a theoretical model relating these characteristics to GTAs' beliefs was developed. To gather data to test the model, the Graduate Students' Experiences Teaching Statistics (GETS) Inventory was created. Items were written to collect information about GTAs' current teaching beliefs, teaching beliefs before entering their degree programs, characteristics of GTAs' experiences in CoPs, and demographic information. Using an online program, the GETS Inventory was administered to N =218 statistics graduate students representing 37 institutions in 24 different U.S. states

A statistical model for instable thermodynamical systems

International Nuclear Information System (INIS)

Sommer, Jens-Uwe

2003-01-01

A generic model is presented for statistical systems which display thermodynamic features in contrast to our everyday experience, such as infinite and negative heat capacities. Such system are instable in terms of classical equilibrium thermodynamics. Using our statistical model, we are able to investigate states of instable systems which are undefined in the framework of equilibrium thermodynamics. We show that a region of negative heat capacity in the adiabatic environment, leads to a first order like phase transition when the system is coupled to a heat reservoir. This phase transition takes place without a phase coexistence. Nevertheless, all intermediate states are stable due to fluctuations. When two instable system are brought in thermal contact, the temperature of the composed system is lower than the minimum temperature of the individual systems. Generally, the equilibrium states of instable system cannot be simply decomposed into equilibrium states of the individual systems. The properties of instable system depend on the environment, ensemble equivalence is broken

Dose-rate and temperature dependent statistical damage accumulation model for ion implantation into silicon

Energy Technology Data Exchange (ETDEWEB)

Hernandez-Mangas, J.M. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain)]. E-mail: jesus.hernandez.mangas@tel.uva.es; Arias, J. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain); Marques, L.A. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain); Ruiz-Bueno, A. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain); Bailon, L. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain)

2005-01-01

Currently there are extensive atomistic studies that model some characteristics of the damage buildup due to ion irradiation (e.g. L. Pelaz et al., Appl. Phys. Lett. 82 (2003) 2038-2040). Our interest is to develop a novel statistical damage buildup model for our BCA ion implant simulator (IIS) code in order to extend its ranges of applicability. The model takes into account the abrupt regime of the crystal-amorphous transition. It works with different temperatures and dose-rates and also models the transition temperature. We have tested it with some projectiles (Ge, P) implanted into silicon. In this work we describe the new statistical damage accumulation model based on the modified Kinchin-Pease model. The results obtained have been compared with existing experimental results.

Dose-rate and temperature dependent statistical damage accumulation model for ion implantation into silicon

International Nuclear Information System (INIS)

Hernandez-Mangas, J.M.; Arias, J.; Marques, L.A.; Ruiz-Bueno, A.; Bailon, L.

2005-01-01

Currently there are extensive atomistic studies that model some characteristics of the damage buildup due to ion irradiation (e.g. L. Pelaz et al., Appl. Phys. Lett. 82 (2003) 2038-2040). Our interest is to develop a novel statistical damage buildup model for our BCA ion implant simulator (IIS) code in order to extend its ranges of applicability. The model takes into account the abrupt regime of the crystal-amorphous transition. It works with different temperatures and dose-rates and also models the transition temperature. We have tested it with some projectiles (Ge, P) implanted into silicon. In this work we describe the new statistical damage accumulation model based on the modified Kinchin-Pease model. The results obtained have been compared with existing experimental results

Model Accuracy Comparison for High Resolution Insar Coherence Statistics Over Urban Areas

Science.gov (United States)

Zhang, Yue; Fu, Kun; Sun, Xian; Xu, Guangluan; Wang, Hongqi

2016-06-01

The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR) images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR) coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.

MODEL ACCURACY COMPARISON FOR HIGH RESOLUTION INSAR COHERENCE STATISTICS OVER URBAN AREAS

Directory of Open Access Journals (Sweden)

Y. Zhang

2016-06-01

Full Text Available The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.

Probability of identification: a statistical model for the validation of qualitative botanical identification methods.

Science.gov (United States)

LaBudde, Robert A; Harnly, James M

2012-01-01

A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.

Statistical Modelling of Resonant Cross Section Structure in URR, Model of the Characteristic Function

International Nuclear Information System (INIS)

Koyumdjieva, N.

2006-01-01

A statistical model for the resonant cross section structure in the Unresolved Resonance Region has been developed in the framework of the R-matrix formalism in Reich Moore approach with effective accounting of the resonance parameters fluctuations. The model uses only the average resonance parameters and can be effectively applied for analyses of cross sections functional, averaged over many resonances. Those are cross section moments, transmission and self-indication functions measured through thick sample. In this statistical model the resonant cross sections structure is accepted to be periodic and the R-matrix is a function of ε=E/D with period 0≤ε≤N; R nc (ε)=π/2√(S n *S c )1/NΣ(i=1,N)(β in *β ic *ctg[π(ε i - = ε-iS i )/N]; Here S n ,S c ,S i is respectively neutron strength function, strength function for fission or inelastic channel and strength function for radiative capture, N is the number of resonances (ε i ,β i ) that obey the statistic of Porter-Thomas and Wigner's one. The simple case of this statistical model concerns the resonant cross section structure for non-fissile nuclei under the threshold for inelastic scattering - the model of the characteristic function with HARFOR program. In the above model some improvements of calculation of the phases and logarithmic derivatives of neutron channels have been done. In the parameterization we use the free parameter R l ∞ , which accounts the influence of long-distant resonances. The above scheme for statistical modelling of the resonant cross section structure has been applied for evaluation of experimental data for total, capture and inelastic cross sections for 232 Th in the URR (4-150) keV and also the transmission and self-indication functions in (4-175) keV. The set of evaluated average resonance parameters have been obtained. The evaluated average resonance parameters in the URR are consistent with those in the Resolved Resonance Region (CRP for Th-U cycle, Vienna, 2006

PVeStA: A Parallel Statistical Model Checking and Quantitative Analysis Tool

KAUST Repository

AlTurki, Musab

2011-01-01

Statistical model checking is an attractive formal analysis method for probabilistic systems such as, for example, cyber-physical systems which are often probabilistic in nature. This paper is about drastically increasing the scalability of statistical model checking, and making such scalability of analysis available to tools like Maude, where probabilistic systems can be specified at a high level as probabilistic rewrite theories. It presents PVeStA, an extension and parallelization of the VeStA statistical model checking tool [10]. PVeStA supports statistical model checking of probabilistic real-time systems specified as either: (i) discrete or continuous Markov Chains; or (ii) probabilistic rewrite theories in Maude. Furthermore, the properties that it can model check can be expressed in either: (i) PCTL/CSL, or (ii) the QuaTEx quantitative temporal logic. As our experiments show, the performance gains obtained from parallelization can be very high. © 2011 Springer-Verlag.

Statistical Method to Overcome Overfitting Issue in Rational Function Models

Science.gov (United States)

Alizadeh Moghaddam, S. H.; Mokhtarzade, M.; Alizadeh Naeini, A.; Alizadeh Moghaddam, S. A.

2017-09-01

Rational function models (RFMs) are known as one of the most appealing models which are extensively applied in geometric correction of satellite images and map production. Overfitting is a common issue, in the case of terrain dependent RFMs, that degrades the accuracy of RFMs-derived geospatial products. This issue, resulting from the high number of RFMs' parameters, leads to ill-posedness of the RFMs. To tackle this problem, in this study, a fast and robust statistical approach is proposed and compared to Tikhonov regularization (TR) method, as a frequently-used solution to RFMs' overfitting. In the proposed method, a statistical test, namely, significance test is applied to search for the RFMs' parameters that are resistant against overfitting issue. The performance of the proposed method was evaluated for two real data sets of Cartosat-1 satellite images. The obtained results demonstrate the efficiency of the proposed method in term of the achievable level of accuracy. This technique, indeed, shows an improvement of 50-80% over the TR.

Statistical principles for prospective study protocols:

DEFF Research Database (Denmark)

Christensen, Robin; Langberg, Henning

2012-01-01

In the design of scientific studies it is essential to decide on which scientific questions one aims to answer, just as it is important to decide on the correct statistical methods to use to answer these questions. The correct use of statistical methods is crucial in all aspects of research...... to quantify relationships in data. Despite an increased focus on statistical content and complexity of biomedical research these topics remain difficult for most researchers. Statistical methods enable researchers to condense large spreadsheets with data into means, proportions, and difference between means...... the statistical principles for trial protocols in terms of design, analysis, and reporting of findings....

A New Equivalent Statistical Damage Constitutive Model on Rock Block Mixed Up with Fluid Inclusions

Directory of Open Access Journals (Sweden)

Xiao Chen

2018-01-01

Full Text Available So far, there are few studies concerning the effect of closed “fluid inclusions” on the macroscopic constitutive relation of deep rock. Fluid-matrix element (FME is defined based on rock element in statistical damage model. The properties of FME are related to the size of inclusions, fluid properties, and pore pressure. Using FME, the equivalent elastic modulus of rock block containing fluid inclusions is obtained with Eshelby inclusion theory and the double M-T homogenization method. The new statistical damage model of rock is established on the equivalent elastic modulus. Besides, the porosity and confining pressure are important influencing factors of the model. The model reflects the initial damage (void and fluid inclusion and the macroscopic deformation law of rock, which is an improvement of the traditional statistical damage model. Additionally, the model can not only be consistent with the rock damage experiment date and three-axis compression experiment date of rock containing pore water but also describe the locked-in stress experiment in rock-like material. It is a new fundamental study of the constitutive relation of locked-in stress in deep rock mass.

Inclusion of temperature dependence of fission barriers in statistical model calculations

International Nuclear Information System (INIS)

Newton, J.O.; Popescu, D.G.; Leigh, J.R.

1990-08-01

The temperature dependence of fission barriers has been interpolated from the results of recent theoretical calculations and included in the statistical model code PACE2. It is shown that the inclusion of temperature dependence causes significant changes to the values of the statistical model parameters deduced from fits to experimental data. 21 refs., 2 figs

Statistical Downscaling of Temperature with the Random Forest Model

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Bo Pang

2017-01-01

Full Text Available The issues with downscaling the outputs of a global climate model (GCM to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA and partial correlation analysis (PAR were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.

A Census of Statistics Requirements at U.S. Journalism Programs and a Model for a "Statistics for Journalism" Course

Science.gov (United States)

Martin, Justin D.

2017-01-01

This essay presents data from a census of statistics requirements and offerings at all 4-year journalism programs in the United States (N = 369) and proposes a model of a potential course in statistics for journalism majors. The author proposes that three philosophies underlie a statistics course for journalism students. Such a course should (a)…

Central Limit Theorem for Exponentially Quasi-local Statistics of Spin Models on Cayley Graphs

Science.gov (United States)

Reddy, Tulasi Ram; Vadlamani, Sreekar; Yogeshwaran, D.

2018-04-01

Central limit theorems for linear statistics of lattice random fields (including spin models) are usually proven under suitable mixing conditions or quasi-associativity. Many interesting examples of spin models do not satisfy mixing conditions, and on the other hand, it does not seem easy to show central limit theorem for local statistics via quasi-associativity. In this work, we prove general central limit theorems for local statistics and exponentially quasi-local statistics of spin models on discrete Cayley graphs with polynomial growth. Further, we supplement these results by proving similar central limit theorems for random fields on discrete Cayley graphs taking values in a countable space, but under the stronger assumptions of α -mixing (for local statistics) and exponential α -mixing (for exponentially quasi-local statistics). All our central limit theorems assume a suitable variance lower bound like many others in the literature. We illustrate our general central limit theorem with specific examples of lattice spin models and statistics arising in computational topology, statistical physics and random networks. Examples of clustering spin models include quasi-associated spin models with fast decaying covariances like the off-critical Ising model, level sets of Gaussian random fields with fast decaying covariances like the massive Gaussian free field and determinantal point processes with fast decaying kernels. Examples of local statistics include intrinsic volumes, face counts, component counts of random cubical complexes while exponentially quasi-local statistics include nearest neighbour distances in spin models and Betti numbers of sub-critical random cubical complexes.

Structural reliability in context of statistical uncertainties and modelling discrepancies

International Nuclear Information System (INIS)

Pendola, Maurice

2000-01-01

Structural reliability methods have been largely improved during the last years and have showed their ability to deal with uncertainties during the design stage or to optimize the functioning and the maintenance of industrial installations. They are based on a mechanical modeling of the structural behavior according to the considered failure modes and on a probabilistic representation of input parameters of this modeling. In practice, only limited statistical information is available to build the probabilistic representation and different sophistication levels of the mechanical modeling may be introduced. Thus, besides the physical randomness, other uncertainties occur in such analyses. The aim of this work is triple: 1. at first, to propose a methodology able to characterize the statistical uncertainties due to the limited number of data in order to take them into account in the reliability analyses. The obtained reliability index measures the confidence in the structure considering the statistical information available. 2. Then, to show a methodology leading to reliability results evaluated from a particular mechanical modeling but by using a less sophisticated one. The objective is then to decrease the computational efforts required by the reference modeling. 3. Finally, to propose partial safety factors that are evolving as a function of the number of statistical data available and as a function of the sophistication level of the mechanical modeling that is used. The concepts are illustrated in the case of a welded pipe and in the case of a natural draught cooling tower. The results show the interest of the methodologies in an industrial context. [fr

Lectures on algebraic statistics

CERN Document Server

Drton, Mathias; Sullivant, Seth

2009-01-01

How does an algebraic geometer studying secant varieties further the understanding of hypothesis tests in statistics? Why would a statistician working on factor analysis raise open problems about determinantal varieties? Connections of this type are at the heart of the new field of "algebraic statistics". In this field, mathematicians and statisticians come together to solve statistical inference problems using concepts from algebraic geometry as well as related computational and combinatorial techniques. The goal of these lectures is to introduce newcomers from the different camps to algebraic statistics. The introduction will be centered around the following three observations: many important statistical models correspond to algebraic or semi-algebraic sets of parameters; the geometry of these parameter spaces determines the behaviour of widely used statistical inference procedures; computational algebraic geometry can be used to study parameter spaces and other features of statistical models.

Directional statistics-based reflectance model for isotropic bidirectional reflectance distribution functions.

Science.gov (United States)

Nishino, Ko; Lombardi, Stephen

2011-01-01

We introduce a novel parametric bidirectional reflectance distribution function (BRDF) model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics distribution, which we refer to as the hemispherical exponential power distribution, and model real-world isotropic BRDFs as mixtures of it. We derive a canonical probabilistic method for estimating the parameters, including the number of components, of this novel directional statistics BRDF model. We show that the model captures the full spectrum of real-world isotropic BRDFs with high accuracy, but a small footprint. We also demonstrate the advantages of the novel BRDF model by showing its use for reflection component separation and for exploring the space of isotropic BRDFs.

Linear mixed-effects models for central statistical monitoring of multicenter clinical trials

OpenAIRE

Desmet, L.; Venet, D.; Doffagne, E.; Timmermans, C.; BURZYKOWSKI, Tomasz; LEGRAND, Catherine; BUYSE, Marc

2014-01-01

Multicenter studies are widely used to meet accrual targets in clinical trials. Clinical data monitoring is required to ensure the quality and validity of the data gathered across centers. One approach to this end is central statistical monitoring, which aims at detecting atypical patterns in the data by means of statistical methods. In this context, we consider the simple case of a continuous variable, and we propose a detection procedure based on a linear mixed-effects model to detect locat...

Operational benefits and challenges of the use of fingerprint statistical models: a field study.

Science.gov (United States)

Neumann, Cedric; Mateos-Garcia, Ismael; Langenburg, Glenn; Kostroski, Jennifer; Skerrett, James E; Koolen, Martin

2011-10-10

Research projects aimed at proposing fingerprint statistical models based on the likelihood ratio framework have shown that low quality finger impressions left on crime scenes may have significant evidential value. These impressions are currently either not recovered, considered to be of no value when first analyzed by fingerprint examiners, or lead to inconclusive results when compared to control prints. There are growing concerns within the fingerprint community that recovering and examining these low quality impressions will result in a significant increase of the workload of fingerprint units and ultimately of the number of backlogged cases. This study was designed to measure the number of impressions currently not recovered or not considered for examination, and to assess the usefulness of these impressions in terms of the number of additional detections that would result from their examination. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Statistical approach for selection of regression model during validation of bioanalytical method

Directory of Open Access Journals (Sweden)

Natalija Nakov

2014-06-01

Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling.

Science.gov (United States)

Cressie, Noel; Calder, Catherine A; Clark, James S; Ver Hoef, Jay M; Wikle, Christopher K

2009-04-01

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

texreg: Conversion of Statistical Model Output in R to LATEX and HTML Tables

Directory of Open Access Journals (Sweden)

Philip Leifeld

2013-11-01

Full Text Available A recurrent task in applied statistics is the (mostly manual preparation of model output for inclusion in LATEX, Microsoft Word, or HTML documents usually with more than one model presented in a single table along with several goodness-of-fit statistics. However, statistical models in R have diverse object structures and summary methods, which makes this process cumbersome. This article first develops a set of guidelines for converting statistical model output to LATEX and HTML tables, then assesses to what extent existing packages meet these requirements, and finally presents the texreg package as a solution that meets all of the criteria set out in the beginning. After providing various usage examples, a blueprint for writing custom model extensions is proposed.

A statistical modeling approach to build expert credit risk rating systems

DEFF Research Database (Denmark)

Waagepetersen, Rasmus

2010-01-01

This paper presents an efficient method for extracting expert knowledge when building a credit risk rating system. Experts are asked to rate a sample of counterparty cases according to creditworthiness. Next, a statistical model is used to capture the relation between the characteristics...... of a counterparty and the expert rating. For any counterparty the model can identify the rating, which would be agreed upon by the majority of experts. Furthermore, the model can quantify the concurrence among experts. The approach is illustrated by a case study regarding the construction of an application score...

Bayesian statistic methods and theri application in probabilistic simulation models

Directory of Open Access Journals (Sweden)

Sergio Iannazzo

2007-03-01

Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.

A classical statistical model of heavy ion collisions

International Nuclear Information System (INIS)

Schmidt, R.; Teichert, J.

1980-01-01

The use of the computer code TRAJEC which represents the numerical realization of a classical statistical model for heavy ion collisions is described. The code calculates the results of a classical friction model as well as various multi-differential cross sections for heavy ion collisions. INPUT and OUTPUT information of the code are described. Two examples of data sets are given [ru

The GNASH preequilibrium-statistical nuclear model code

International Nuclear Information System (INIS)

Arthur, E. D.

1988-01-01

The following report is based on materials presented in a series of lectures at the International Center for Theoretical Physics, Trieste, which were designed to describe the GNASH preequilibrium statistical model code and its use. An overview is provided of the code with emphasis upon code's calculational capabilities and the theoretical models that have been implemented in it. Two sample problems are discussed, the first dealing with neutron reactions on 58 Ni. the second illustrates the fission model capabilities implemented in the code and involves n + 235 U reactions. Finally a description is provided of current theoretical model and code development underway. Examples of calculated results using these new capabilities are also given. 19 refs., 17 figs., 3 tabs

Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking

DEFF Research Database (Denmark)

ter Beek, Maurice H.; Legay, Axel; Lluch Lafuente, Alberto

2015-01-01

We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLAN with action rates, which specify the likelihood of exhibiting pa...

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students.

Science.gov (United States)

Saadati, Farzaneh; Ahmad Tarmizi, Rohani; Mohd Ayub, Ahmad Fauzi; Abu Bakar, Kamariah

2015-01-01

Because students' ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is 'value added' because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students' problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students.

Percolation for a model of statistically inhomogeneous random media

International Nuclear Information System (INIS)

Quintanilla, J.; Torquato, S.

1999-01-01

We study clustering and percolation phenomena for a model of statistically inhomogeneous two-phase random media, including functionally graded materials. This model consists of inhomogeneous fully penetrable (Poisson distributed) disks and can be constructed for any specified variation of volume fraction. We quantify the transition zone in the model, defined by the frontier of the cluster of disks which are connected to the disk-covered portion of the model, by defining the coastline function and correlation functions for the coastline. We find that the behavior of these functions becomes largely independent of the specific choice of grade in volume fraction as the separation of length scales becomes large. We also show that the correlation function behaves in a manner similar to that of fractal Brownian motion. Finally, we study fractal characteristics of the frontier itself and compare to similar properties for two-dimensional percolation on a lattice. In particular, we show that the average location of the frontier appears to be related to the percolation threshold for homogeneous fully penetrable disks. copyright 1999 American Institute of Physics

The Statistical Modeling of the Trends Concerning the Romanian Population

Directory of Open Access Journals (Sweden)

Gabriela OPAIT

2014-11-01

Full Text Available This paper reflects the statistical modeling concerning the resident population in Romania, respectively the total of the romanian population, through by means of the „Least Squares Method”. Any country it develops by increasing of the population, respectively of the workforce, which is a factor of influence for the growth of the Gross Domestic Product (G.D.P.. The „Least Squares Method” represents a statistical technique for to determine the trend line of the best fit concerning a model.

Statistical models for thermal ageing of steel materials in nuclear power plants

International Nuclear Information System (INIS)

Persoz, M.

1996-01-01

Some category of steel materials in nuclear power plants may be subjected to thermal ageing, whose extent depends on the steel chemical composition and the ageing parameters, i.e. temperature and duration. This ageing affects the 'impact strength' of the materials, which is a mechanical property. In order to assess the residual lifetime of these components, a probabilistic study has been launched, which takes into account the scatter over the input parameters of the mechanical model. Predictive formulae for estimating the impact strength of aged materials are important input data of the model. A data base has been created with impact strength results obtained from an ageing program in laboratory and statistical treatments have been undertaken. Two kinds of model have been developed, with non linear regression methods (PROC NLIN, available in SAS/STAT). The first one, using a hyperbolic tangent function, is partly based on physical considerations, and the second one, of an exponential type, is purely statistically built. The difficulties consist in selecting the significant parameters and attributing initial values to the coefficients, which is a requirement of the NLIN procedure. This global statistical analysis has led to general models that are unction of the chemical variables and the ageing parameters. These models are as precise (if not more) as local models that had been developed earlier for some specific values of ageing temperature and ageing duration. This paper describes the data and the methodology used to build the models and analyses the results given by the SAS system. (author)

Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization.

Science.gov (United States)

Kim, Seongho; Li, Lang

2014-02-01

The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Sound statistical model checking for MDP using partial order and confluence reduction

NARCIS (Netherlands)

Hartmanns, Arnd; Timmer, Mark

Statistical model checking (SMC) is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can in general only provide sound

Application of Statistical Model in Wastewater Treatment Process Modeling Using Data Analysis

Directory of Open Access Journals (Sweden)

Alireza Raygan Shirazinezhad

2015-06-01

Full Text Available Background: Wastewater treatment includes very complex and interrelated physical, chemical and biological processes which using data analysis techniques can be rigorously modeled by a non-complex mathematical calculation models. Materials and Methods: In this study, data on wastewater treatment processes from water and wastewater company of Kohgiluyeh and Boyer Ahmad were used. A total of 3306 data for COD, TSS, PH and turbidity were collected, then analyzed by SPSS-16 software (descriptive statistics and data analysis IBM SPSS Modeler 14.2, through 9 algorithm. Results: According to the results on logistic regression algorithms, neural networks, Bayesian networks, discriminant analysis, decision tree C5, tree C & R, CHAID, QUEST and SVM had accuracy precision of 90.16, 94.17, 81.37, 70.48, 97.89, 96.56, 96.46, 96.84 and 88.92, respectively. Discussion and conclusion: The C5 algorithm as the best and most applicable algorithms for modeling of wastewater treatment processes were chosen carefully with accuracy of 97.899 and the most influential variables in this model were PH, COD, TSS and turbidity.

Statistical modeling to support power system planning

Science.gov (United States)

Staid, Andrea

This dissertation focuses on data-analytic approaches that improve our understanding of power system applications to promote better decision-making. It tackles issues of risk analysis, uncertainty management, resource estimation, and the impacts of climate change. Tools of data mining and statistical modeling are used to bring new insight to a variety of complex problems facing today's power system. The overarching goal of this research is to improve the understanding of the power system risk environment for improved operation, investment, and planning decisions. The first chapter introduces some challenges faced in planning for a sustainable power system. Chapter 2 analyzes the driving factors behind the disparity in wind energy investments among states with a goal of determining the impact that state-level policies have on incentivizing wind energy. Findings show that policy differences do not explain the disparities; physical and geographical factors are more important. Chapter 3 extends conventional wind forecasting to a risk-based focus of predicting maximum wind speeds, which are dangerous for offshore operations. Statistical models are presented that issue probabilistic predictions for the highest wind speed expected in a three-hour interval. These models achieve a high degree of accuracy and their use can improve safety and reliability in practice. Chapter 4 examines the challenges of wind power estimation for onshore wind farms. Several methods for wind power resource assessment are compared, and the weaknesses of the Jensen model are demonstrated. For two onshore farms, statistical models outperform other methods, even when very little information is known about the wind farm. Lastly, chapter 5 focuses on the power system more broadly in the context of the risks expected from tropical cyclones in a changing climate. Risks to U.S. power system infrastructure are simulated under different scenarios of tropical cyclone behavior that may result from climate

Study design and statistical analysis of data in human population studies with the micronucleus assay.

Science.gov (United States)

Ceppi, Marcello; Gallo, Fabio; Bonassi, Stefano

2011-01-01

The most common study design performed in population studies based on the micronucleus (MN) assay, is the cross-sectional study, which is largely performed to evaluate the DNA damaging effects of exposure to genotoxic agents in the workplace, in the environment, as well as from diet or lifestyle factors. Sample size is still a critical issue in the design of MN studies since most recent studies considering gene-environment interaction, often require a sample size of several hundred subjects, which is in many cases difficult to achieve. The control of confounding is another major threat to the validity of causal inference. The most popular confounders considered in population studies using MN are age, gender and smoking habit. Extensive attention is given to the assessment of effect modification, given the increasing inclusion of biomarkers of genetic susceptibility in the study design. Selected issues concerning the statistical treatment of data have been addressed in this mini-review, starting from data description, which is a critical step of statistical analysis, since it allows to detect possible errors in the dataset to be analysed and to check the validity of assumptions required for more complex analyses. Basic issues dealing with statistical analysis of biomarkers are extensively evaluated, including methods to explore the dose-response relationship among two continuous variables and inferential analysis. A critical approach to the use of parametric and non-parametric methods is presented, before addressing the issue of most suitable multivariate models to fit MN data. In the last decade, the quality of statistical analysis of MN data has certainly evolved, although even nowadays only a small number of studies apply the Poisson model, which is the most suitable method for the analysis of MN data.

Enhanced surrogate models for statistical design exploiting space mapping technology

DEFF Research Database (Denmark)

Koziel, Slawek; Bandler, John W.; Mohamed, Achmed S.

2005-01-01

We present advances in microwave and RF device modeling exploiting Space Mapping (SM) technology. We propose new SM modeling formulations utilizing input mappings, output mappings, frequency scaling and quadratic approximations. Our aim is to enhance circuit models for statistical analysis...

A statistical study on fracture toughness data of Japanese RPVS

International Nuclear Information System (INIS)

Sakai, Y.; Ogura, N.

1987-01-01

In a cooperative study for investigating fracture toughness on pressure vessel steels produced in Japan, a number of heats of ASTM A533B cl.1 and A508 cl.3 steels have been studied. Approximately 3000 fracture toughness data and 8000 mechanical properties data were obtained and filed in a computer data bank. Statistical characterization of toughness data in the transition region has been carried out using the computer data bank. Curve fitting technique for toughness data has been examined. Approach using the function to model the transition behaviours of each toughness has been applied. The aims of fitting curve technique were as follows; (1) Summarization of an enormous toughness data base to permit comparison heats, materials and testing methods; (2) Investigating the relationships among static, dynamic and arrest toughness; (3) Examining the ASME K(IR) curve statistically. The methodology used in this study for analyzing a large quantity of fracture toughness data was found to be useful for formulating a statistically based K(IR) curve. (orig./HP)

Logarithmic transformed statistical models in calibration

International Nuclear Information System (INIS)

Zeis, C.D.

1975-01-01

A general type of statistical model used for calibration of instruments having the property that the standard deviations of the observed values increase as a function of the mean value is described. The application to the Helix Counter at the Rocky Flats Plant is primarily from a theoretical point of view. The Helix Counter measures the amount of plutonium in certain types of chemicals. The method described can be used also for other calibrations. (U.S.)

Fast optimization of statistical potentials for structurally constrained phylogenetic models

Directory of Open Access Journals (Sweden)

Rodrigue Nicolas

2009-09-01

Full Text Available Abstract Background Statistical approaches for protein design are relevant in the field of molecular evolutionary studies. In recent years, new, so-called structurally constrained (SC models of protein-coding sequence evolution have been proposed, which use statistical potentials to assess sequence-structure compatibility. In a previous work, we defined a statistical framework for optimizing knowledge-based potentials especially suited to SC models. Our method used the maximum likelihood principle and provided what we call the joint potentials. However, the method required numerical estimations by the use of computationally heavy Markov Chain Monte Carlo sampling algorithms. Results Here, we develop an alternative optimization procedure, based on a leave-one-out argument coupled to fast gradient descent algorithms. We assess that the leave-one-out potential yields very similar results to the joint approach developed previously, both in terms of the resulting potential parameters, and by Bayes factor evaluation in a phylogenetic context. On the other hand, the leave-one-out approach results in a considerable computational benefit (up to a 1,000 fold decrease in computational time for the optimization procedure. Conclusion Due to its computational speed, the optimization method we propose offers an attractive alternative for the design and empirical evaluation of alternative forms of potentials, using large data sets and high-dimensional parameterizations.

Trends in study design and the statistical methods employed in a leading general medicine journal.

Science.gov (United States)

Gosho, M; Sato, Y; Nagashima, K; Takahashi, S

2018-02-01

Study design and statistical methods have become core components of medical research, and the methodology has become more multifaceted and complicated over time. The study of the comprehensive details and current trends of study design and statistical methods is required to support the future implementation of well-planned clinical studies providing information about evidence-based medicine. Our purpose was to illustrate study design and statistical methods employed in recent medical literature. This was an extension study of Sato et al. (N Engl J Med 2017; 376: 1086-1087), which reviewed 238 articles published in 2015 in the New England Journal of Medicine (NEJM) and briefly summarized the statistical methods employed in NEJM. Using the same database, we performed a new investigation of the detailed trends in study design and individual statistical methods that were not reported in the Sato study. Due to the CONSORT statement, prespecification and justification of sample size are obligatory in planning intervention studies. Although standard survival methods (eg Kaplan-Meier estimator and Cox regression model) were most frequently applied, the Gray test and Fine-Gray proportional hazard model for considering competing risks were sometimes used for a more valid statistical inference. With respect to handling missing data, model-based methods, which are valid for missing-at-random data, were more frequently used than single imputation methods. These methods are not recommended as a primary analysis, but they have been applied in many clinical trials. Group sequential design with interim analyses was one of the standard designs, and novel design, such as adaptive dose selection and sample size re-estimation, was sometimes employed in NEJM. Model-based approaches for handling missing data should replace single imputation methods for primary analysis in the light of the information found in some publications. Use of adaptive design with interim analyses is increasing

Statistical validation of normal tissue complication probability models

NARCIS (Netherlands)

Xu, Cheng-Jian; van der Schaaf, Arjen; van t Veld, Aart; Langendijk, Johannes A.; Schilstra, Cornelis

2012-01-01

PURPOSE: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: A penalized regression method, LASSO (least absolute shrinkage

Statistical thermodynamics

International Nuclear Information System (INIS)

Lim, Gyeong Hui

2008-03-01

This book consists of 15 chapters, which are basic conception and meaning of statistical thermodynamics, Maxwell-Boltzmann's statistics, ensemble, thermodynamics function and fluctuation, statistical dynamics with independent particle system, ideal molecular system, chemical equilibrium and chemical reaction rate in ideal gas mixture, classical statistical thermodynamics, ideal lattice model, lattice statistics and nonideal lattice model, imperfect gas theory on liquid, theory on solution, statistical thermodynamics of interface, statistical thermodynamics of a high molecule system and quantum statistics

The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

Science.gov (United States)

Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

2013-10-01

Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

Statistics of 2D solitons

International Nuclear Information System (INIS)

Brekke, L.; Imbo, T.D.

1992-01-01

The authors study the inequivalent quantizations of (1 + 1)-dimensional nonlinear sigma models with space manifold S 1 and target manifold X. If x is multiply connected, these models possess topological solitons. After providing a definition of spin and statistics for these solitons and demonstrating a spin-statistics correlation, we give various examples where the solitons can have exotic statistics. In some of these models, the solitons may obey a generalized version of fractional statistics called ambistatistics. In this paper the relevance of these 2d models to the statistics of vortices in (2 + 1)-dimensional spontaneously broken gauge theories is discussed. The authors close with a discussion concerning the extension of our results to higher dimensions

Statistical modelling of networked human-automation performance using working memory capacity.

Science.gov (United States)

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

Statistical Maps of Ground Magnetic Disturbance Derived from Global Geospace Models

Science.gov (United States)

Rigler, E. J.; Wiltberger, M. J.; Love, J. J.

2017-12-01

Electric currents in space are the principal driver of magnetic variations measured at Earth's surface. These in turn induce geoelectric fields that present a natural hazard for technological systems like high-voltage power distribution networks. Modern global geospace models can reasonably simulate large-scale geomagnetic response to solar wind variations, but they are less successful at deterministic predictions of intense localized geomagnetic activity that most impacts technological systems on the ground. Still, recent studies have shown that these models can accurately reproduce the spatial statistical distributions of geomagnetic activity, suggesting that their physics are largely correct. Since the magnetosphere is a largely externally driven system, most model-measurement discrepancies probably arise from uncertain boundary conditions. So, with realistic distributions of solar wind parameters to establish its boundary conditions, we use the Lyon-Fedder-Mobarry (LFM) geospace model to build a synthetic multivariate statistical model of gridded ground magnetic disturbance. From this, we analyze the spatial modes of geomagnetic response, regress on available measurements to fill in unsampled locations on the grid, and estimate the global probability distribution of extreme magnetic disturbance. The latter offers a prototype geomagnetic "hazard map", similar to those used to characterize better-known geophysical hazards like earthquakes and floods.

A statistical model for investigating binding probabilities of DNA nucleotide sequences using microarrays.

Science.gov (United States)

Lee, Mei-Ling Ting; Bulyk, Martha L; Whitmore, G A; Church, George M

2002-12-01

There is considerable scientific interest in knowing the probability that a site-specific transcription factor will bind to a given DNA sequence. Microarray methods provide an effective means for assessing the binding affinities of a large number of DNA sequences as demonstrated by Bulyk et al. (2001, Proceedings of the National Academy of Sciences, USA 98, 7158-7163) in their study of the DNA-binding specificities of Zif268 zinc fingers using microarray technology. In a follow-up investigation, Bulyk, Johnson, and Church (2002, Nucleic Acid Research 30, 1255-1261) studied the interdependence of nucleotides on the binding affinities of transcription proteins. Our article is motivated by this pair of studies. We present a general statistical methodology for analyzing microarray intensity measurements reflecting DNA-protein interactions. The log probability of a protein binding to a DNA sequence on an array is modeled using a linear ANOVA model. This model is convenient because it employs familiar statistical concepts and procedures and also because it is effective for investigating the probability structure of the binding mechanism.

Accurate corresponding point search using sphere-attribute-image for statistical bone model generation

International Nuclear Information System (INIS)

Saito, Toki; Nakajima, Yoshikazu; Sugita, Naohiko; Mitsuishi, Mamoru; Hashizume, Hiroyuki; Kuramoto, Kouichi; Nakashima, Yosio

2011-01-01

Statistical deformable model based two-dimensional/three-dimensional (2-D/3-D) registration is a promising method for estimating the position and shape of patient bone in the surgical space. Since its accuracy depends on the statistical model capacity, we propose a method for accurately generating a statistical bone model from a CT volume. Our method employs the Sphere-Attribute-Image (SAI) and has improved the accuracy of corresponding point search in statistical model generation. At first, target bone surfaces are extracted as SAIs from the CT volume. Then the textures of SAIs are classified to some regions using Maximally-stable-extremal-regions methods. Next, corresponding regions are determined using Normalized cross-correlation (NCC). Finally, corresponding points in each corresponding region are determined using NCC. The application of our method to femur bone models was performed, and worked well in the experiments. (author)

Mathematical-statistical model for analysis of Ulva algal net photosynthesis in Venice lagoon

International Nuclear Information System (INIS)

Izzo, G.; Rizzo, V.; Bella, A.; Picci, M.; Giordano, P.

1996-08-01

The algal net photosynthesis, an important factor for the characterization of water quality in Venice lagoon, has been studied experimentally providing a mathematical model, validated by using statistical methods. This model relates oxygen production with irradiance, according to a well known law in biological literature. Its observed an inverted proportion between algal oxygen production and temperature, thus seasonality

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models

International Nuclear Information System (INIS)

Lovejoy, S.; Lima, M. I. P. de

2015-01-01

Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time

Monitor-Based Statistical Model Checking for Weighted Metric Temporal Logic

DEFF Research Database (Denmark)

Bulychev, Petr; David, Alexandre; Larsen, Kim Guldstrand

2012-01-01

We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction with desi......We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction...

Statistical Model of the 2001 Czech Census for Interactive Presentation

Czech Academy of Sciences Publication Activity Database

Grim, Jiří; Hora, Jan; Boček, Pavel; Somol, Petr; Pudil, Pavel

Vol. 26, č. 4 (2010), s. 1-23 ISSN 0282-423X R&D Projects: GA ČR GA102/07/1594; GA MŠk 1M0572 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Interactive statistical model * census data presentation * distribution mixtures * data modeling * EM algorithm * incomplete data * data reproduction accuracy * data mining Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.492, year: 2010 http://library.utia.cas.cz/separaty/2010/RO/grim-0350513.pdf

Statistical model predictions for p+p and Pb+Pb collisions at LHC

NARCIS (Netherlands)

Kraus, I.; Cleymans, J.; Oeschler, H.; Redlich, K.; Wheaton, S.

2009-01-01

Particle production in p+p and central collisions at LHC is discussed in the context of the statistical thermal model. For heavy-ion collisions, predictions of various particle ratios are presented. The sensitivity of several ratios on the temperature and the baryon chemical potential is studied in

Exploiting linkage disequilibrium in statistical modelling in quantitative genomics

DEFF Research Database (Denmark)

Wang, Lei

Alleles at two loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Genomic prediction and gene mapping rely on the existence of LD between gentic markers and causul variants of complex traits. In the first part of the thesis, a novel method...... to quantify and visualize local variation in LD along chromosomes in describet, and applied to characterize LD patters at the local and genome-wide scale in three Danish pig breeds. In the second part, different ways of taking LD into account in genomic prediction models are studied. One approach is to use...... the recently proposed antedependence models, which treat neighbouring marker effects as correlated; another approach involves use of haplotype block information derived using the program Beagle. The overall conclusion is that taking LD information into account in genomic prediction models potentially improves...

A testing procedure for wind turbine generators based on the power grid statistical model

DEFF Research Database (Denmark)

Farajzadehbibalan, Saber; Ramezani, Mohammad Hossein; Nielsen, Peter

2017-01-01

In this study, a comprehensive test procedure is developed to test wind turbine generators with a hardware-in-loop setup. The procedure employs the statistical model of the power grid considering the restrictions of the test facility and system dynamics. Given the model in the latent space...

Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions.

Science.gov (United States)

Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J

2016-05-01

Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . © 2015 John Wiley & Sons Ltd/London School of Economics.

A statistical mechanical model of economics

Science.gov (United States)

Lubbers, Nicholas Edward Williams

Statistical mechanics pursues low-dimensional descriptions of systems with a very large number of degrees of freedom. I explore this theme in two contexts. The main body of this dissertation explores and extends the Yard Sale Model (YSM) of economic transactions using a combination of simulations and theory. The YSM is a simple interacting model for wealth distributions which has the potential to explain the empirical observation of Pareto distributions of wealth. I develop the link between wealth condensation and the breakdown of ergodicity due to nonlinear diffusion effects which are analogous to the geometric random walk. Using this, I develop a deterministic effective theory of wealth transfer in the YSM that is useful for explaining many quantitative results. I introduce various forms of growth to the model, paying attention to the effect of growth on wealth condensation, inequality, and ergodicity. Arithmetic growth is found to partially break condensation, and geometric growth is found to completely break condensation. Further generalizations of geometric growth with growth in- equality show that the system is divided into two phases by a tipping point in the inequality parameter. The tipping point marks the line between systems which are ergodic and systems which exhibit wealth condensation. I explore generalizations of the YSM transaction scheme to arbitrary betting functions to develop notions of universality in YSM-like models. I find that wealth vi condensation is universal to a large class of models which can be divided into two phases. The first exhibits slow, power-law condensation dynamics, and the second exhibits fast, finite-time condensation dynamics. I find that the YSM, which exhibits exponential dynamics, is the critical, self-similar model which marks the dividing line between the two phases. The final chapter develops a low-dimensional approach to materials microstructure quantification. Modern materials design harnesses complex

Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos

DEFF Research Database (Denmark)

Ganz, Melanie; Nielsen, Mads; Brandt, Sami

2010-01-01

We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in x-ray radio graphs. The generative model is constructed by (1) learning ...

A Quantitative Comparative Study of Blended and Traditional Models in the Secondary Advanced Placement Statistics Classroom

Science.gov (United States)

Owens, Susan T.

2017-01-01

Technology is becoming an integral tool in the classroom and can make a positive impact on how the students learn. This quantitative comparative research study examined gender-based differences among secondary Advanced Placement (AP) Statistic students comparing Educational Testing Service (ETS) College Board AP Statistic examination scores…

Snow cover and End of Summer Snowline statistics from a simple stochastic model

Science.gov (United States)

Petrelli, A.; Crouzy, B.; Perona, P.

2012-04-01

One essential parameter characterizing snow cover statistics is the End Of Summer Snowline (EOSS), which is also a good indicator of actual climatic trends in mountain regions. EOSS is usually modelled by means of spatially distributed physically based models, and typically require heavy parameterization. In this paper we validate the simple stochastic model proposed by Perona et al. (2007), by showing that the snow cover statistics and the position of EOSS can in principle be explained by only four essential (meteorological) parameters. Perona et al. (2007) proposed a model accounting for stochastic snow accumulation in the cold period, and deterministic melting dynamics in the warm period, and studied the statistical distribution of the snowdepth on the long term. By reworking the ensemble average of the steady state evolution equation we single out a relationship between the snowdepth statistics (including the position of EOSS) and the involved parameters. The validation of the established relationship is done using 50 years of field data from 73 Swiss stations located above 2000 m a.s.l. First an estimation of the meteorological parameters is made. Snow height data are used as a precipitation proxy, using temperature data to estimate SWE during the precipitation event. Thresholds are used both to separate accumulation from actual precipitation and wind transport phenomena, and to better assess summer melting rate, considered to be constant over the melting period according to the simplified model. First results show that data for most of the weather stations actually scales with the proposed relationship. This indicates that, on the long term, the effect of spatial and temporal noise masks most of the process detail so that minimalist models suffice to obtain reliable statistics. Future works will test the validity of this approach at different spatial scales, e.g., regional, continental and planetary. Reference: P. Perona, A. Porporato, and L. Ridolfi, "A

Estimating preferential flow in karstic aquifers using statistical mixed models.

Science.gov (United States)

Anaya, Angel A; Padilla, Ingrid; Macchiavelli, Raul; Vesper, Dorothy J; Meeker, John D; Alshawabkeh, Akram N

2014-01-01

Karst aquifers are highly productive groundwater systems often associated with conduit flow. These systems can be highly vulnerable to contamination, resulting in a high potential for contaminant exposure to humans and ecosystems. This work develops statistical models to spatially characterize flow and transport patterns in karstified limestone and determines the effect of aquifer flow rates on these patterns. A laboratory-scale Geo-HydroBed model is used to simulate flow and transport processes in a karstic limestone unit. The model consists of stainless steel tanks containing a karstified limestone block collected from a karst aquifer formation in northern Puerto Rico. Experimental work involves making a series of flow and tracer injections, while monitoring hydraulic and tracer response spatially and temporally. Statistical mixed models (SMMs) are applied to hydraulic data to determine likely pathways of preferential flow in the limestone units. The models indicate a highly heterogeneous system with dominant, flow-dependent preferential flow regions. Results indicate that regions of preferential flow tend to expand at higher groundwater flow rates, suggesting a greater volume of the system being flushed by flowing water at higher rates. Spatial and temporal distribution of tracer concentrations indicates the presence of conduit-like and diffuse flow transport in the system, supporting the notion of both combined transport mechanisms in the limestone unit. The temporal response of tracer concentrations at different locations in the model coincide with, and confirms the preferential flow distribution generated with the SMMs used in the study. © 2013, National Ground Water Association.

Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems.

Science.gov (United States)

Sapsis, Themistoklis P; Majda, Andrew J

2013-08-20

A framework for low-order predictive statistical modeling and uncertainty quantification in turbulent dynamical systems is developed here. These reduced-order, modified quasilinear Gaussian (ROMQG) algorithms apply to turbulent dynamical systems in which there is significant linear instability or linear nonnormal dynamics in the unperturbed system and energy-conserving nonlinear interactions that transfer energy from the unstable modes to the stable modes where dissipation occurs, resulting in a statistical steady state; such turbulent dynamical systems are ubiquitous in geophysical and engineering turbulence. The ROMQG method involves constructing a low-order, nonlinear, dynamical system for the mean and covariance statistics in the reduced subspace that has the unperturbed statistics as a stable fixed point and optimally incorporates the indirect effect of non-Gaussian third-order statistics for the unperturbed system in a systematic calibration stage. This calibration procedure is achieved through information involving only the mean and covariance statistics for the unperturbed equilibrium. The performance of the ROMQG algorithm is assessed on two stringent test cases: the 40-mode Lorenz 96 model mimicking midlatitude atmospheric turbulence and two-layer baroclinic models for high-latitude ocean turbulence with over 125,000 degrees of freedom. In the Lorenz 96 model, the ROMQG algorithm with just a single mode captures the transient response to random or deterministic forcing. For the baroclinic ocean turbulence models, the inexpensive ROMQG algorithm with 252 modes, less than 0.2% of the total, captures the nonlinear response of the energy, the heat flux, and even the one-dimensional energy and heat flux spectra.

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM on Statistics Learning among Postgraduate Students.

Directory of Open Access Journals (Sweden)

Farzaneh Saadati

Full Text Available Because students' ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is 'value added' because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students' problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students.

Document Categorization with Modified Statistical Language Models for Agglutinative Languages

Directory of Open Access Journals (Sweden)

Tantug

2010-11-01

Full Text Available In this paper, we investigate the document categorization task with statistical language models. Our study mainly focuses on categorization of documents in agglutinative languages. Due to the productive morphology of agglutinative languages, the number of word forms encountered in naturally occurring text is very large. From the language modeling perspective, a large vocabulary results in serious data sparseness problems. In order to cope with this drawback, previous studies in various application areas suggest modified language models based on different morphological units. It is reported that performance improvements can be achieved with these modified language models. In our document categorization experiments, we use standard word form based language models as well as other modified language models based on root words, root words and part-of-speech information, truncated word forms and character sequences. Additionally, to find an optimum parameter set, multiple tests are carried out with different language model orders and smoothing methods. Similar to previous studies on other tasks, our experimental results on categorization of Turkish documents reveal that applying linguistic preprocessing steps for language modeling provides improvements over standard language models to some extent. However, it is also observed that similar level of performance improvements can also be acquired by simpler character level or truncated word form models which are language independent.

Statistical aspects of carbon fiber risk assessment modeling. [fire accidents involving aircraft

Science.gov (United States)

Gross, D.; Miller, D. R.; Soland, R. M.

1980-01-01

The probabilistic and statistical aspects of the carbon fiber risk assessment modeling of fire accidents involving commercial aircraft are examined. Three major sources of uncertainty in the modeling effort are identified. These are: (1) imprecise knowledge in establishing the model; (2) parameter estimation; and (3)Monte Carlo sampling error. All three sources of uncertainty are treated and statistical procedures are utilized and/or developed to control them wherever possible.

Model output statistics applied to wind power prediction

Energy Technology Data Exchange (ETDEWEB)

Joensen, A; Giebel, G; Landberg, L [Risoe National Lab., Roskilde (Denmark); Madsen, H; Nielsen, H A [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

1999-03-01

Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

Modeling of Dissipation Element Statistics in Turbulent Non-Premixed Jet Flames

Science.gov (United States)

Denker, Dominik; Attili, Antonio; Boschung, Jonas; Hennig, Fabian; Pitsch, Heinz

2017-11-01

The dissipation element (DE) analysis is a method for analyzing and compartmentalizing turbulent scalar fields. DEs can be described by two parameters, namely the Euclidean distance l between their extremal points and the scalar difference in the respective points Δϕ . The joint probability density function (jPDF) of these two parameters P(Δϕ , l) is expected to suffice for a statistical reconstruction of the scalar field. In addition, reacting scalars show a strong correlation with these DE parameters in both premixed and non-premixed flames. Normalized DE statistics show a remarkable invariance towards changes in Reynolds numbers. This feature of DE statistics was exploited in a Boltzmann-type evolution equation based model for the probability density function (PDF) of the distance between the extremal points P(l) in isotropic turbulence. Later, this model was extended for the jPDF P(Δϕ , l) and then adapted for the use in free shear flows. The effect of heat release on the scalar scales and DE statistics is investigated and an extended model for non-premixed jet flames is introduced, which accounts for the presence of chemical reactions. This new model is validated against a series of DNS of temporally evolving jet flames. European Research Council Project ``Milestone''.

The relationship between the C-statistic of a risk-adjustment model and the accuracy of hospital report cards: a Monte Carlo Study.

Science.gov (United States)

Austin, Peter C; Reeves, Mathew J

2013-03-01

Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is risk-adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for risk adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards. To determine the relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards. Monte Carlo simulations were used to examine this issue. We examined the influence of 3 factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for risk adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada. The c-statistic of the risk-adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact. The c-statistic of a risk-adjustment model should not be used to assess the accuracy of a hospital report card.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

Science.gov (United States)

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

α -induced reactions on 115In: Cross section measurements and statistical model analysis

Science.gov (United States)

Kiss, G. G.; Szücs, T.; Mohr, P.; Török, Zs.; Huszánk, R.; Gyürky, Gy.; Fülöp, Zs.

2018-05-01

Background: α -nucleus optical potentials are basic ingredients of statistical model calculations used in nucleosynthesis simulations. While the nucleon+nucleus optical potential is fairly well known, for the α +nucleus optical potential several different parameter sets exist and large deviations, reaching sometimes even an order of magnitude, are found between the cross section predictions calculated using different parameter sets. Purpose: A measurement of the radiative α -capture and the α -induced reaction cross sections on the nucleus 115In at low energies allows a stringent test of statistical model predictions. Since experimental data are scarce in this mass region, this measurement can be an important input to test the global applicability of α +nucleus optical model potentials and further ingredients of the statistical model. Methods: The reaction cross sections were measured by means of the activation method. The produced activities were determined by off-line detection of the γ rays and characteristic x rays emitted during the electron capture decay of the produced Sb isotopes. The 115In(α ,γ )119Sb and 115In(α ,n )Sb118m reaction cross sections were measured between Ec .m .=8.83 and 15.58 MeV, and the 115In(α ,n )Sb118g reaction was studied between Ec .m .=11.10 and 15.58 MeV. The theoretical analysis was performed within the statistical model. Results: The simultaneous measurement of the (α ,γ ) and (α ,n ) cross sections allowed us to determine a best-fit combination of all parameters for the statistical model. The α +nucleus optical potential is identified as the most important input for the statistical model. The best fit is obtained for the new Atomki-V1 potential, and good reproduction of the experimental data is also achieved for the first version of the Demetriou potentials and the simple McFadden-Satchler potential. The nucleon optical potential, the γ -ray strength function, and the level density parametrization are also

Model selection for contingency tables with algebraic statistics

NARCIS (Netherlands)

Krampe, A.; Kuhnt, S.; Gibilisco, P.; Riccimagno, E.; Rogantin, M.P.; Wynn, H.P.

2009-01-01

Goodness-of-fit tests based on chi-square approximations are commonly used in the analysis of contingency tables. Results from algebraic statistics combined with MCMC methods provide alternatives to the chi-square approximation. However, within a model selection procedure usually a large number of

Sensitivity study of experimental measures for the nuclear liquid-gas phase transition in the statistical multifragmentation model

Science.gov (United States)

Lin, W.; Ren, P.; Zheng, H.; Liu, X.; Huang, M.; Wada, R.; Qu, G.

2018-05-01

The experimental measures of the multiplicity derivatives—the moment parameters, the bimodal parameter, the fluctuation of maximum fragment charge number (normalized variance of Zmax, or NVZ), the Fisher exponent (τ ), and the Zipf law parameter (ξ )—are examined to search for the liquid-gas phase transition in nuclear multifragmention processes within the framework of the statistical multifragmentation model (SMM). The sensitivities of these measures are studied. All these measures predict a critical signature at or near to the critical point both for the primary and secondary fragments. Among these measures, the total multiplicity derivative and the NVZ provide accurate measures for the critical point from the final cold fragments as well as the primary fragments. The present study will provide a guide for future experiments and analyses in the study of the nuclear liquid-gas phase transition.

α-ternary decay of Cf isotopes, statistical model

International Nuclear Information System (INIS)

Joseph, Jayesh George; Santhosh, K.P.

2017-01-01

The process of splitting a heavier nucleus to three simultaneous fragments is termed as ternary fission and compared to usual binary fission, it is a rare process. Depending on the nature of third particle either it is called light charged particle (LCP) accompanying fission if it is light or true ternary fission if all three fragments have nearly same mass distributions. After experimental observations in early seventies, initially with a slow pace, now theoretical studies in ternary fission has turned to a hot topic in nuclear decay studies especially in past one decade. Mean while various models have been developed, existing being modified and seeking for new with a hope that it can beam a little more light to the profound nature of nuclear interaction. In this study a statistical method, level density formulation, has been employed

Integer Set Compression and Statistical Modeling

DEFF Research Database (Denmark)

Larsson, N. Jesper

2014-01-01

enumeration of elements may be arbitrary or random, but where statistics is kept in order to estimate probabilities of elements. We present a recursive subset-size encoding method that is able to benefit from statistics, explore the effects of permuting the enumeration order based on element probabilities......Compression of integer sets and sequences has been extensively studied for settings where elements follow a uniform probability distribution. In addition, methods exist that exploit clustering of elements in order to achieve higher compression performance. In this work, we address the case where...

Probing the exchange statistics of one-dimensional anyon models

Science.gov (United States)

Greschner, Sebastian; Cardarelli, Lorenzo; Santos, Luis

2018-05-01

We propose feasible scenarios for revealing the modified exchange statistics in one-dimensional anyon models in optical lattices based on an extension of the multicolor lattice-depth modulation scheme introduced in [Phys. Rev. A 94, 023615 (2016), 10.1103/PhysRevA.94.023615]. We show that the fast modulation of a two-component fermionic lattice gas in the presence a magnetic field gradient, in combination with additional resonant microwave fields, allows for the quantum simulation of hardcore anyon models with periodic boundary conditions. Such a semisynthetic ring setup allows for realizing an interferometric arrangement sensitive to the anyonic statistics. Moreover, we show as well that simple expansion experiments may reveal the formation of anomalously bound pairs resulting from the anyonic exchange.

Critical, statistical, and thermodynamical properties of lattice models

Energy Technology Data Exchange (ETDEWEB)

Varma, Vipin Kerala

2013-10-15

In this thesis we investigate zero temperature and low temperature properties - critical, statistical and thermodynamical - of lattice models in the contexts of bosonic cold atom systems, magnetic materials, and non-interacting particles on various lattice geometries. We study quantum phase transitions in the Bose-Hubbard model with higher body interactions, as relevant for optical lattice experiments of strongly interacting bosons, in one and two dimensions; the universality of the Mott insulator to superfluid transition is found to remain unchanged for even large three body interaction strengths. A systematic renormalization procedure is formulated to fully re-sum these higher (three and four) body interactions into the two body terms. In the strongly repulsive limit, we analyse the zero and low temperature physics of interacting hard-core bosons on the kagome lattice at various fillings. Evidence for a disordered phase in the Ising limit of the model is presented; in the strong coupling limit, the transition between the valence bond solid and the superfluid is argued to be first order at the tip of the solid lobe.

Critical, statistical, and thermodynamical properties of lattice models

International Nuclear Information System (INIS)

Varma, Vipin Kerala

2013-10-01

In this thesis we investigate zero temperature and low temperature properties - critical, statistical and thermodynamical - of lattice models in the contexts of bosonic cold atom systems, magnetic materials, and non-interacting particles on various lattice geometries. We study quantum phase transitions in the Bose-Hubbard model with higher body interactions, as relevant for optical lattice experiments of strongly interacting bosons, in one and two dimensions; the universality of the Mott insulator to superfluid transition is found to remain unchanged for even large three body interaction strengths. A systematic renormalization procedure is formulated to fully re-sum these higher (three and four) body interactions into the two body terms. In the strongly repulsive limit, we analyse the zero and low temperature physics of interacting hard-core bosons on the kagome lattice at various fillings. Evidence for a disordered phase in the Ising limit of the model is presented; in the strong coupling limit, the transition between the valence bond solid and the superfluid is argued to be first order at the tip of the solid lobe.

Statistical studies of energetic electrons in the outer radiation belt

Energy Technology Data Exchange (ETDEWEB)

Johnstone, A.D.; Rodgers, D.J.; Jones, G.H. E-mail: g.h.jones@ic.ac.uk

1999-10-01

The medium electron A (MEA) instrument aboard the CRRES spacecraft provided data on terrestrial radiation belt electrons in the energy range from 153 to 1582 keV, during 1990-91. These data have previously been used to produce an empirical model of the radiation belts from L=1.1 to 8.9, ordered according to 17 energy bands, 18 pitch angle bins, and 5 Kp ranges. Empirical models such as this are very valuable, but are prone to statistical fluctuations and gaps in coverage. In this study, in order to smooth the data and make it more easy to interpolate within data gaps, the pitch angle distribution at each energy in the model was fitted with a Bessel function. This provided a way to characterize the pitch angle in terms of only two parameters for each energy. It was not possible to model fluxes reliably within the loss cone because of poor statistics. The fitted distributions give an indication of the way in which pitch angle diffusion varies in the outer radiation belts. The two parameters of the Bessel function were found to vary systematically with L value, energy and Kp. Through the fitting of a simple function to these systematic variations, the number of parameters required to describe the model could be reduced drastically.

Study of relationship between MUF correlation and detection sensitivity of statistical analysis

International Nuclear Information System (INIS)

Tamura, Toshiaki; Ihara, Hitoshi; Yamamoto, Yoichi; Ikawa, Koji

1989-11-01

Various kinds of statistical analysis are proposed to NRTA (Near Real Time Materials Accountancy) which was devised to satisfy the timeliness goal of one of the detection goals of IAEA. It will be presumed that different statistical analysis results will occur between the case of considered rigorous error propagation (with MUF correlation) and the case of simplified error propagation (without MUF correlation). Therefore, measurement simulation and decision analysis were done using flow simulation of 800 MTHM/Y model reprocessing plant, and relationship between MUF correlation and detection sensitivity and false alarm of statistical analysis was studied. Specific character of material accountancy for 800 MTHM/Y model reprocessing plant was grasped by this simulation. It also became clear that MUF correlation decreases not only false alarm but also detection probability for protracted loss in case of CUMUF test and Page's test applied to NRTA. (author)

Mask effects on cosmological studies with weak-lensing peak statistics

International Nuclear Information System (INIS)

Liu, Xiangkun; Pan, Chuzhong; Fan, Zuhui; Wang, Qiao

2014-01-01

With numerical simulations, we analyze in detail how the bad data removal, i.e., the mask effect, can influence the peak statistics of the weak-lensing convergence field reconstructed from the shear measurement of background galaxies. It is found that high peak fractions are systematically enhanced because of the presence of masks; the larger the masked area is, the higher the enhancement is. In the case where the total masked area is about 13% of the survey area, the fraction of peaks with signal-to-noise ratio ν ≥ 3 is ∼11% of the total number of peaks, compared with ∼7% of the mask-free case in our considered cosmological model. This can have significant effects on cosmological studies with weak-lensing convergence peak statistics, inducing a large bias in the parameter constraints if the effects are not taken into account properly. Even for a survey area of 9 deg 2 , the bias in (Ω m , σ 8 ) is already intolerably large and close to 3σ. It is noted that most of the affected peaks are close to the masked regions. Therefore, excluding peaks in those regions in the peak statistics can reduce the bias effect but at the expense of losing usable survey areas. Further investigations find that the enhancement of the number of high peaks around the masked regions can be largely attributed to the smaller number of galaxies usable in the weak-lensing convergence reconstruction, leading to higher noise than that of the areas away from the masks. We thus develop a model in which we exclude only those very large masks with radius larger than 3' but keep all the other masked regions in peak counting statistics. For the remaining part, we treat the areas close to and away from the masked regions separately with different noise levels. It is shown that this two-noise-level model can account for the mask effect on peak statistics very well, and the bias in cosmological parameters is significantly reduced if this model is applied in the parameter fitting.

Automatic generation of statistical pose and shape models for articulated joints.

Science.gov (United States)

Xin Chen; Graham, Jim; Hutchinson, Charles; Muir, Lindsay

2014-02-01

Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of 0.34 ±0.27 mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.

Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation.

Science.gov (United States)

Hart, Carl R; Reznicek, Nathan J; Wilson, D Keith; Pettit, Chris L; Nykaza, Edward T

2016-05-01

Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations. In the simulated data set sound propagation conditions span from downward refracting to upward refracting, for acoustically hard and soft boundaries, and low frequencies. Engineering models used in the comparisons include the ISO 9613-2 method, Harmonoise, and Nord2000 propagation models. Statistical learning methods used in the comparisons include bagged decision tree regression, random forest regression, boosting regression, and artificial neural network models. Computed skill scores are relative to sound propagation in a homogeneous atmosphere over a rigid ground. Overall skill scores for the engineering noise models are 0.6%, -7.1%, and 83.8% for the ISO 9613-2, Harmonoise, and Nord2000 models, respectively. Overall skill scores for the statistical learning models are 99.5%, 99.5%, 99.6%, and 99.6% for bagged decision tree, random forest, boosting, and artificial neural network regression models, respectively.

What type of statistical model to choose for the analysis of radioimmunoassays

International Nuclear Information System (INIS)

Huet, S.

1984-01-01

The current techniques used for statistical analysis of radioimmunoassays are not very satisfactory for either the statistician or the biologist. They are based on an attempt to make the response curve linear to avoid complicated computations. The present article shows that this practice has considerable effects (often neglected) on the statistical assumptions which must be formulated. A more strict analysis is proposed by applying the four-parameter logistic model. The advantages of this method are: the statistical assumptions formulated are based on observed data, and the model can be applied to almost all radioimmunoassays [fr

Craniofacial Statistical Deformation Models of Wild-type mice and Crouzon mice

DEFF Research Database (Denmark)

Ólafsdóttir, Hildur; Darvann, Tron Andre; Ersbøll, Bjarne Kjær

2007-01-01

Crouzon syndrome is characterised by the premature fusion of cranial sutures and synchondroses leading to craniofacial growth disturbances. The gene causing the syndrome was discovered approximately a decade ago and recently the first mouse model of the syndrome was generated. In this study, a set...... of Micro CT scannings of the heads of wild-type (normal) mice and Crouzon mice were investigated. Statistical deformation models were built to assess the anatomical differences between the groups, as well as the within-group anatomical variation. Following the approach by Rueckert et al. we built an atlas...

Statistical properties of several models of fractional random point processes

Science.gov (United States)

Bendjaballah, C.

2011-08-01

Statistical properties of several models of fractional random point processes have been analyzed from the counting and time interval statistics points of view. Based on the criterion of the reduced variance, it is seen that such processes exhibit nonclassical properties. The conditions for these processes to be treated as conditional Poisson processes are examined. Numerical simulations illustrate part of the theoretical calculations.

A Mediation Model to Explain the Role of Mathematics Skills and Probabilistic Reasoning on Statistics Achievement

Science.gov (United States)

Primi, Caterina; Donati, Maria Anna; Chiesi, Francesca

2016-01-01

Among the wide range of factors related to the acquisition of statistical knowledge, competence in basic mathematics, including basic probability, has received much attention. In this study, a mediation model was estimated to derive the total, direct, and indirect effects of mathematical competence on statistics achievement taking into account…

Statistical Model Predictions for p+p and Pb+Pb Collisions at LHC

CERN Document Server

Kraus, I; Oeschler, H; Redlich, K; Wheaton, S

2009-01-01

Particle production in p+p and central Pb+Pb collisions at LHC is discussed in the context of the statistical thermal model. For heavy-ion collisions, predictions of various particle ratios are presented. The sensitivity of several ratios on the temperature and the baryon chemical potential is studied in detail, and some of them, which are particularly appropriate to determine the chemical freeze-out point experimentally, are indicated. Considering elementary interactions on the other hand, we focus on strangeness production and its possible suppression. Extrapolating the thermal parameters to LHC energy, we present predictions of the statistical model for particle yields in p+p collisions. We quantify the strangeness suppression by the correlation volume parameter and discuss its influence on particle production. We propose observables that can provide deeper insight into the mechanism of strangeness production and suppression at LHC.

Statistical Modeling of Energy Production by Photovoltaic Farms

Czech Academy of Sciences Publication Activity Database

Brabec, Marek; Pelikán, Emil; Krč, Pavel; Eben, Kryštof; Musílek, P.

2011-01-01

Roč. 5, č. 9 (2011), s. 785-793 ISSN 1934-8975 Grant - others:GA AV ČR(CZ) M100300904 Institutional research plan: CEZ:AV0Z10300504 Keywords : electrical energy * solar energy * numerical weather prediction model * nonparametric regression * beta regression Subject RIV: BB - Applied Statistics, Operational Research

Mathematical-statistical models and qualitative theories for economic and social sciences

CERN Document Server

Maturo, Fabrizio; Kacprzyk, Janusz

2017-01-01

This book presents a broad spectrum of problems related to statistics, mathematics, teaching, social science, and economics as well as a range of tools and techniques that can be used to solve these problems. It is the result of a scientific collaboration between experts in the field of economic and social systems from the University of Defence in Brno (Czech Republic), G. d’Annunzio University of Chieti-Pescara (Italy), Pablo de Olavid eUniversity of Sevilla (Spain), and Ovidius University in Constanţa, (Romania). The studies included were selected using a peer-review process and reflect heterogeneity and complexity of economic and social phenomena. They and present interesting empirical research from around the globe and from several research fields, such as statistics, decision making, mathematics, complexity, psychology, sociology and economics. The volume is divided into two parts. The first part, “Recent trends in mathematical and statistical models for economic and social sciences”, collects pap...

Statistical field theory of futures commodity prices

Science.gov (United States)

Baaquie, Belal E.; Yu, Miao

2018-02-01

The statistical theory of commodity prices has been formulated by Baaquie (2013). Further empirical studies of single (Baaquie et al., 2015) and multiple commodity prices (Baaquie et al., 2016) have provided strong evidence in support the primary assumptions of the statistical formulation. In this paper, the model for spot prices (Baaquie, 2013) is extended to model futures commodity prices using a statistical field theory of futures commodity prices. The futures prices are modeled as a two dimensional statistical field and a nonlinear Lagrangian is postulated. Empirical studies provide clear evidence in support of the model, with many nontrivial features of the model finding unexpected support from market data.

An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

International Nuclear Information System (INIS)

Weathers, J.B.; Luck, R.; Weathers, J.W.

2009-01-01

The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

Energy Technology Data Exchange (ETDEWEB)

Weathers, J.B. [Shock, Noise, and Vibration Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: James.Weathers@ngc.com; Luck, R. [Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box ME, Mississippi State, MS 39762-5925 (United States)], E-mail: Luck@me.msstate.edu; Weathers, J.W. [Structural Analysis Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: Jeffrey.Weathers@ngc.com

2009-11-15

The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

Study of beta-delayed neutron with proton-neutron QRPA plus statistical model

International Nuclear Information System (INIS)

Minato, Futoshi; Iwamoto, Osamu

2015-01-01

β-delayed neutron is known to be important for safety operation of nuclear reactor and prediction of elemental abundance after freeze-out of r-process. A lot of researches on it have been performed. However, the experimental data are far from complete since the lifetime of most of the relevant nuclei is so short that one cannot measure in a high efficiency. In order to estimate half-lives and delayed neutron emission probabilities of unexplored nuclei, we developed a new theoretical method which combines a proton-neutron quasi-particle random-phase-approximation and the Hauser-Feshbach statistical model. The present method reproduces experimentally known β-decay half-lives within a factor of 10 and about 40% of within a factor of 2. However it fails to reproduce delayed neutron emission probabilities. We discuss the problems and remedy for them to be made in future. (author)

Modelling diversity in building occupant behaviour: a novel statistical approach

DEFF Research Database (Denmark)

Haldi, Frédéric; Calì, Davide; Andersen, Rune Korsholm

2016-01-01

We propose an advanced modelling framework to predict the scope and effects of behavioural diversity regarding building occupant actions on window openings, shading devices and lighting. We develop a statistical approach based on generalised linear mixed models to account for the longitudinal nat...

A Unified Statistical Rain-Attenuation Model for Communication Link Fade Predictions and Optimal Stochastic Fade Control Design Using a Location-Dependent Rain-Statistic Database

Science.gov (United States)

Manning, Robert M.

1990-01-01

A static and dynamic rain-attenuation model is presented which describes the statistics of attenuation on an arbitrarily specified satellite link for any location for which there are long-term rainfall statistics. The model may be used in the design of the optimal stochastic control algorithms to mitigate the effects of attenuation and maintain link reliability. A rain-statistics data base is compiled, which makes it possible to apply the model to any location in the continental U.S. with a resolution of 0-5 degrees in latitude and longitude. The model predictions are compared with experimental observations, showing good agreement.

Effect of model choice and sample size on statistical tolerance limits

International Nuclear Information System (INIS)

Duran, B.S.; Campbell, K.

1980-03-01

Statistical tolerance limits are estimates of large (or small) quantiles of a distribution, quantities which are very sensitive to the shape of the tail of the distribution. The exact nature of this tail behavior cannot be ascertained brom small samples, so statistical tolerance limits are frequently computed using a statistical model chosen on the basis of theoretical considerations or prior experience with similar populations. This report illustrates the effects of such choices on the computations

Statistical properties of the nuclear shell-model Hamiltonian

International Nuclear Information System (INIS)

Dias, H.; Hussein, M.S.; Oliveira, N.A. de

1986-01-01

The statistical properties of realistic nuclear shell-model Hamiltonian are investigated in sd-shell nuclei. The probability distribution of the basic-vector amplitude is calculated and compared with the Porter-Thomas distribution. Relevance of the results to the calculation of the giant resonance mixing parameter is pointed out. (Author) [pt

On the statistical comparison of climate model output and climate data

International Nuclear Information System (INIS)

Solow, A.R.

1991-01-01

Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question. 9 refs

On the statistical comparison of climate model output and climate data

International Nuclear Information System (INIS)

Solow, A.R.

1990-01-01

Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

Science.gov (United States)

Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Statistical Methods for Unusual Count Data: Examples From Studies of Microchimerism

Science.gov (United States)

Guthrie, Katherine A.; Gammill, Hilary S.; Kamper-Jørgensen, Mads; Tjønneland, Anne; Gadi, Vijayakrishna K.; Nelson, J. Lee; Leisenring, Wendy

2016-01-01

Natural acquisition of small amounts of foreign cells or DNA, referred to as microchimerism, occurs primarily through maternal-fetal exchange during pregnancy. Microchimerism can persist long-term and has been associated with both beneficial and adverse human health outcomes. Quantitative microchimerism data present challenges for statistical analysis, including a skewed distribution, excess zero values, and occasional large values. Methods for comparing microchimerism levels across groups while controlling for covariates are not well established. We compared statistical models for quantitative microchimerism values, applied to simulated data sets and 2 observed data sets, to make recommendations for analytic practice. Modeling the level of quantitative microchimerism as a rate via Poisson or negative binomial model with the rate of detection defined as a count of microchimerism genome equivalents per total cell equivalents tested utilizes all available data and facilitates a comparison of rates between groups. We found that both the marginalized zero-inflated Poisson model and the negative binomial model can provide unbiased and consistent estimates of the overall association of exposure or study group with microchimerism detection rates. The negative binomial model remains the more accessible of these 2 approaches; thus, we conclude that the negative binomial model may be most appropriate for analyzing quantitative microchimerism data. PMID:27769989

Statistical Models to Assess the Health Effects and to Forecast Ground Level Ozone

Czech Academy of Sciences Publication Activity Database

Schlink, U.; Herbath, O.; Richter, M.; Dorling, S.; Nunnari, G.; Cawley, G.; Pelikán, Emil

2006-01-01

Roč. 21, č. 4 (2006), s. 547-558 ISSN 1364-8152 R&D Projects: GA AV ČR 1ET400300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : statistical models * ground level ozone * health effects * logistic model * forecasting * prediction performance * neural network * generalised additive model * integrated assessment Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.992, year: 2006

On an uncorrelated jet model with Bose-Einstein statistics

International Nuclear Information System (INIS)

Bilic, N.; Dadic, I.; Martinis, M.

1978-01-01

Starting from the density of states of an ideal Bose-Einstein gas, an uncorrelated jet model with Bose-Einstein statistics has been formulated. The transition to continuum is based on the Touschek invariant measure. It has been shown that in this model average multiplicity increases logarithmically with total energy, while the inclusive distribution shows ln s violation of scaling. (author)

Efficient pan-European river flood hazard modelling through a combination of statistical and physical models

NARCIS (Netherlands)

Paprotny, D.; Morales Napoles, O.; Jonkman, S.N.

2017-01-01

Flood hazard is currently being researched on continental and global scales, using models of increasing complexity. In this paper we investigate a different, simplified approach, which combines statistical and physical models in place of conventional rainfall-run-off models to carry out flood

A simple statistical model for geomagnetic reversals

Science.gov (United States)

Constable, Catherine

1990-01-01

The diversity of paleomagnetic records of geomagnetic reversals now available indicate that the field configuration during transitions cannot be adequately described by simple zonal or standing field models. A new model described here is based on statistical properties inferred from the present field and is capable of simulating field transitions like those observed. Some insight is obtained into what one can hope to learn from paleomagnetic records. In particular, it is crucial that the effects of smoothing in the remanence acquisition process be separated from true geomagnetic field behavior. This might enable us to determine the time constants associated with the dominant field configuration during a reversal.

Discrete ellipsoidal statistical BGK model and Burnett equations

Science.gov (United States)

Zhang, Yu-Dong; Xu, Ai-Guo; Zhang, Guang-Cai; Chen, Zhi-Hua; Wang, Pei

2018-06-01

A new discrete Boltzmann model, the discrete ellipsoidal statistical Bhatnagar-Gross-Krook (ESBGK) model, is proposed to simulate nonequilibrium compressible flows. Compared with the original discrete BGK model, the discrete ES-BGK has a flexible Prandtl number. For the discrete ES-BGK model in the Burnett level, two kinds of discrete velocity model are introduced and the relations between nonequilibrium quantities and the viscous stress and heat flux in the Burnett level are established. The model is verified via four benchmark tests. In addition, a new idea is introduced to recover the actual distribution function through the macroscopic quantities and their space derivatives. The recovery scheme works not only for discrete Boltzmann simulation but also for hydrodynamic ones, for example, those based on the Navier-Stokes or the Burnett equations.

Study of energy fluctuation effect on the statistical mechanics of equilibrium systems

International Nuclear Information System (INIS)

Lysogorskiy, Yu V; Wang, Q A; Tayurskii, D A

2012-01-01

This work is devoted to the modeling of energy fluctuation effect on the behavior of small classical thermodynamic systems. It is known that when an equilibrium system gets smaller and smaller, one of the major quantities that becomes more and more uncertain is its internal energy. These increasing fluctuations can considerably modify the original statistics. The present model considers the effect of such energy fluctuations and is based on an overlapping between the Boltzmann-Gibbs statistics and the statistics of the fluctuation. Within this o verlap statistics , we studied the effects of several types of energy fluctuations on the probability distribution, internal energy and heat capacity. It was shown that the fluctuations can considerably change the temperature dependence of internal energy and heat capacity in the low energy range and at low temperatures. Particularly, it was found that, due to the lower energy limit of the systems, the fluctuations reduce the probability for the low energy states close to the lowest energy and increase the total average energy. This energy increasing is larger for lower temperatures, making negative heat capacity possible for this case.

Substorm associated radar auroral surges: a statistical study and possible generation model

Directory of Open Access Journals (Sweden)

B. A. Shand

Full Text Available Substorm-associated radar auroral surges (SARAS are a short lived (15–90 minutes and spatially localised (~5° of latitude perturbation of the plasma convection pattern observed within the auroral E-region. The understanding of such phenomena has important ramifications for the investigation of the larger scale plasma convection and ultimately the coupling of the solar wind, magnetosphere and ionosphere system. A statistical investigation is undertaken of SARAS, observed by the Sweden And Britain Radar Experiment (SABRE, in order to provide a more extensive examination of the local time occurrence and propagation characteristics of the events. The statistical analysis has determined a local time occurrence of observations between 1420 MLT and 2200 MLT with a maximum occurrence centred around 1700 MLT. The propagation velocity of the SARAS feature through the SABRE field of view was found to be predominately L-shell aligned with a velocity centred around 1750 m s^–1 and within the range 500 m s^–1 and 3500 m s^–1. This comprehensive examination of the SARAS provides the opportunity to discuss, qualitatively, a possible generation mechanism for SARAS based on a proposed model for the production of a similar phenomenon referred to as sub-auroral ion drifts (SAIDs. The results of the comparison suggests that SARAS may result from a similar geophysical mechanism to that which produces SAID events, but probably occurs at a different time in the evolution of the event.

Key words. Substorms · Auroral surges · Plasma con-vection · Sub-auroral ion drifts

Eigenfunction statistics for Anderson model with Hölder continuous ...

Indian Academy of Sciences (India)

The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India ... Anderson model; Hölder continuous measure; Poisson statistics. ...... [4] Combes J-M, Hislop P D and Klopp F, An optimal Wegner estimate and its application to.

Statistics for X-chromosome associations.

Science.gov (United States)

Özbek, Umut; Lin, Hui-Min; Lin, Yan; Weeks, Daniel E; Chen, Wei; Shaffer, John R; Purcell, Shaun M; Feingold, Eleanor

2018-06-13

In a genome-wide association study (GWAS), association between genotype and phenotype at autosomal loci is generally tested by regression models. However, X-chromosome data are often excluded from published analyses of autosomes because of the difference between males and females in number of X chromosomes. Failure to analyze X-chromosome data at all is obviously less than ideal, and can lead to missed discoveries. Even when X-chromosome data are included, they are often analyzed with suboptimal statistics. Several mathematically sensible statistics for X-chromosome association have been proposed. The optimality of these statistics, however, is based on very specific simple genetic models. In addition, while previous simulation studies of these statistics have been informative, they have focused on single-marker tests and have not considered the types of error that occur even under the null hypothesis when the entire X chromosome is scanned. In this study, we comprehensively tested several X-chromosome association statistics using simulation studies that include the entire chromosome. We also considered a wide range of trait models for sex differences and phenotypic effects of X inactivation. We found that models that do not incorporate a sex effect can have large type I error in some cases. We also found that many of the best statistics perform well even when there are modest deviations, such as trait variance differences between the sexes or small sex differences in allele frequencies, from assumptions. © 2018 WILEY PERIODICALS, INC.

Current fluctuations and statistics during a large deviation event in an exactly solvable transport model

International Nuclear Information System (INIS)

Hurtado, Pablo I; Garrido, Pedro L

2009-01-01

We study the distribution of the time-integrated current in an exactly solvable toy model of heat conduction, both analytically and numerically. The simplicity of the model allows us to derive the full current large deviation function and the system statistics during a large deviation event. In this way we unveil a relation between system statistics at the end of a large deviation event and for intermediate times. The mid-time statistics is independent of the sign of the current, a reflection of the time-reversal symmetry of microscopic dynamics, while the end-time statistics does depend on the current sign, and also on its microscopic definition. We compare our exact results with simulations based on the direct evaluation of large deviation functions, analyzing the finite-size corrections of this simulation method and deriving detailed bounds for its applicability. We also show how the Gallavotti–Cohen fluctuation theorem can be used to determine the range of validity of simulation results

A d-statistic for single-case designs that is equivalent to the usual between-groups d-statistic.

Science.gov (United States)

Shadish, William R; Hedges, Larry V; Pustejovsky, James E; Boyajian, Jonathan G; Sullivan, Kristynn J; Andrade, Alma; Barrientos, Jeannette L

2014-01-01

We describe a standardised mean difference statistic (d) for single-case designs that is equivalent to the usual d in between-groups experiments. We show how it can be used to summarise treatment effects over cases within a study, to do power analyses in planning new studies and grant proposals, and to meta-analyse effects across studies of the same question. We discuss limitations of this d-statistic, and possible remedies to them. Even so, this d-statistic is better founded statistically than other effect size measures for single-case design, and unlike many general linear model approaches such as multilevel modelling or generalised additive models, it produces a standardised effect size that can be integrated over studies with different outcome measures. SPSS macros for both effect size computation and power analysis are available.

A BRDF statistical model applying to space target materials modeling

Science.gov (United States)

Liu, Chenghao; Li, Zhi; Xu, Can; Tian, Qichen

2017-10-01

In order to solve the problem of poor effect in modeling the large density BRDF measured data with five-parameter semi-empirical model, a refined statistical model of BRDF which is suitable for multi-class space target material modeling were proposed. The refined model improved the Torrance-Sparrow model while having the modeling advantages of five-parameter model. Compared with the existing empirical model, the model contains six simple parameters, which can approximate the roughness distribution of the material surface, can approximate the intensity of the Fresnel reflectance phenomenon and the attenuation of the reflected light's brightness with the azimuth angle changes. The model is able to achieve parameter inversion quickly with no extra loss of accuracy. The genetic algorithm was used to invert the parameters of 11 different samples in the space target commonly used materials, and the fitting errors of all materials were below 6%, which were much lower than those of five-parameter model. The effect of the refined model is verified by comparing the fitting results of the three samples at different incident zenith angles in 0° azimuth angle. Finally, the three-dimensional modeling visualizations of these samples in the upper hemisphere space was given, in which the strength of the optical scattering of different materials could be clearly shown. It proved the good describing ability of the refined model at the material characterization as well.

A New Form of Nondestructive Strength-Estimating Statistical Models Accounting for Uncertainty of Model and Aging Effect of Concrete

International Nuclear Information System (INIS)

Hong, Kee Jeung; Kim, Jee Sang

2009-01-01

As concrete ages, the surrounding environment is expected to have growing influences on the concrete. As all the impacts of the environment cannot be considered in the strength-estimating model of a nondestructive concrete test, the increase in concrete age leads to growing uncertainty in the strength-estimating model. Therefore, the variation of the model error increases. It is necessary to include those impacts in the probability model of concrete strength attained from the nondestructive tests so as to build a more accurate reliability model for structural performance evaluation. This paper reviews and categorizes the existing strength-estimating statistical models of nondestructive concrete test, and suggests a new form of the strength-estimating statistical models to properly reflect the model uncertainty due to aging of the concrete. This new form of the statistical models will lay foundation for more accurate structural performance evaluation.

Spatio-temporal statistical models with applications to atmospheric processes

International Nuclear Information System (INIS)

Wikle, C.K.

1996-01-01

This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model

Poisson statistics application in modelling of neutron detection

International Nuclear Information System (INIS)

Avdic, S.; Marinkovic, P.

1996-01-01

The main purpose of this study is taking into account statistical analysis of the experimental data which were measured by 3 He neutron spectrometer. The unfolding method based on principle of maximum likelihood incorporates the Poisson approximation of counting statistics applied (aithor)

Interpretation of the results of statistical measurements. [search for basic probability model

Science.gov (United States)

Olshevskiy, V. V.

1973-01-01

For random processes, the calculated probability characteristic, and the measured statistical estimate are used in a quality functional, which defines the difference between the two functions. Based on the assumption that the statistical measurement procedure is organized so that the parameters for a selected model are optimized, it is shown that the interpretation of experimental research is a search for a basic probability model.

Statistics of the Navier–Stokes-alpha-beta regularization model for fluid turbulence

International Nuclear Information System (INIS)

Hinz, Denis F; Kim, Tae-Yeon; Fried, Eliot

2014-01-01

We explore one-point and two-point statistics of the Navier–Stokes-αβ regularization model at moderate Reynolds number (Re ≈ 200) in homogeneous isotropic turbulence. The results are compared to the limit cases of the Navier–Stokes-α model and the Navier–Stokes-αβ model without subgrid-scale stress, as well as with high-resolution direct numerical simulation. After reviewing spectra of different energy norms of the Navier–Stokes-αβ model, the Navier–Stokes-α model, and Navier–Stokes-αβ model without subgrid-scale stress, we present probability density functions and normalized probability density functions of the filtered and unfiltered velocity increments along with longitudinal velocity structure functions of the regularization models and direct numerical simulation results. We highlight differences in the statistical properties of the unfiltered and filtered velocity fields entering the governing equations of the Navier–Stokes-α and Navier–Stokes-αβ models and discuss the usability of both velocity fields for realistic flow predictions. The influence of the modified viscous term in the Navier–Stokes-αβ model is studied through comparison to the case where the underlying subgrid-scale stress tensor is neglected. Whereas, the filtered velocity field is found to have physically more viable probability density functions and structure functions for the approximation of direct numerical simulation results, the unfiltered velocity field is found to have flatness factors close to direct numerical simulation results. (paper)

An improved mixing model providing joint statistics of scalar and scalar dissipation

Energy Technology Data Exchange (ETDEWEB)

Meyer, Daniel W. [Department of Energy Resources Engineering, Stanford University, Stanford, CA (United States); Jenny, Patrick [Institute of Fluid Dynamics, ETH Zurich (Switzerland)

2008-11-15

For the calculation of nonpremixed turbulent flames with thin reaction zones the joint probability density function (PDF) of the mixture fraction and its dissipation rate plays an important role. The corresponding PDF transport equation involves a mixing model for the closure of the molecular mixing term. Here, the parameterized scalar profile (PSP) mixing model is extended to provide the required joint statistics. Model predictions are validated using direct numerical simulation (DNS) data of a passive scalar mixing in a statistically homogeneous turbulent flow. Comparisons between the DNS and the model predictions are provided, which involve different initial scalar-field lengthscales. (author)

Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks

Directory of Open Access Journals (Sweden)

Luciano Pivoto Specht

2007-03-01

Full Text Available It is of a great importance to know binders' viscosity in order to perform handling, mixing, application processes and asphalt mixes compaction in highway surfacing. This paper presents the results of viscosity measurement in asphalt-rubber binders prepared in laboratory. The binders were prepared varying the rubber content, rubber particle size, duration and temperature of mixture, all following a statistical design plan. The statistical analysis and artificial neural networks were used to create mathematical models for prediction of the binders viscosity. The comparison between experimental data and simulated results with the generated models showed best performance of the neural networks analysis in contrast to the statistic models. The results indicated that the rubber content and duration of mixture have major influence on the observed viscosity for the considered interval of parameters variation.

Comparison of Artificial Neural Networks and ARIMA statistical models in simulations of target wind time series

Science.gov (United States)

Kolokythas, Kostantinos; Vasileios, Salamalikis; Athanassios, Argiriou; Kazantzidis, Andreas

2015-04-01

The wind is a result of complex interactions of numerous mechanisms taking place in small or large scales, so, the better knowledge of its behavior is essential in a variety of applications, especially in the field of power production coming from wind turbines. In the literature there is a considerable number of models, either physical or statistical ones, dealing with the problem of simulation and prediction of wind speed. Among others, Artificial Neural Networks (ANNs) are widely used for the purpose of wind forecasting and, in the great majority of cases, outperform other conventional statistical models. In this study, a number of ANNs with different architectures, which have been created and applied in a dataset of wind time series, are compared to Auto Regressive Integrated Moving Average (ARIMA) statistical models. The data consist of mean hourly wind speeds coming from a wind farm on a hilly Greek region and cover a period of one year (2013). The main goal is to evaluate the models ability to simulate successfully the wind speed at a significant point (target). Goodness-of-fit statistics are performed for the comparison of the different methods. In general, the ANN showed the best performance in the estimation of wind speed prevailing over the ARIMA models.

ARSENIC CONTAMINATION IN GROUNDWATER: A STATISTICAL MODELING

OpenAIRE

Palas Roy; Naba Kumar Mondal; Biswajit Das; Kousik Das

2013-01-01

High arsenic in natural groundwater in most of the tubewells of the Purbasthali- Block II area of Burdwan district (W.B, India) has recently been focused as a serious environmental concern. This paper is intending to illustrate the statistical modeling of the arsenic contaminated groundwater to identify the interrelation of that arsenic contain with other participating groundwater parameters so that the arsenic contamination level can easily be predicted by analyzing only such parameters. Mul...

Modeling the basic superconductor thermodynamical-statistical characteristics

International Nuclear Information System (INIS)

Palenskis, V.; Maknys, K.

1999-01-01

In accordance with the Landau second-order phase transition and other thermodynamical-statistical relations for superconductors, and using the energy gap as an order parameter in the electron free energy presentation, the fundamental characteristics of electrons, such as the free energy, the total energy, the energy gap, the entropy, and the heat capacity dependences on temperature were obtained. The obtained modeling results, in principle, well reflect the basic low- and high-temperature superconductor characteristics

Statistical modeling of the power grid from a wind farm standpoint

DEFF Research Database (Denmark)

Farajzadehbibalan, Saber; Ramezani, Mohammad H.; Nielsen, Peter

2017-01-01

wind farm over several years which results in the development of a useful model for practical purposes. Secondly, the derived model is computationally inexpensive. Considering an arbitrary wind turbine generator, we show that the behavior of the power grid at the connection point can be represented......In this study, we derive a statistical model of a power grid from the wind farm's standpoint based on dynamic principal component analysis. The main advantages of our model compared to the previously developed models are twofold. Firstly, our proposed model benefits from logged data of an offshore...... by 4 out of 9 registered variables, i.e. 3-phase voltages, 3-phase currents, frequency, and generated active and reactive powers. We further prove that the dynamic nature of the system can be optimally captured by a time lag shift of two samples. To extend the derived model of a wind turbine generator...

Using student models to generate feedback in a university course on statistical sampling

NARCIS (Netherlands)

Tacoma, S.G.|info:eu-repo/dai/nl/411923080; Drijvers, P.H.M.|info:eu-repo/dai/nl/074302922; Boon, P.B.J.|info:eu-repo/dai/nl/203374207

2017-01-01

Due to the complexity of the topic and a lack of individual guidance, introductory statistics courses at university are often challenging. Automated feedback might help to address this issue. In this study, we explore the use of student models to provide feedback. The research question is how

Improved air ventilation rate estimation based on a statistical model

International Nuclear Information System (INIS)

Brabec, M.; Jilek, K.

2004-01-01

A new approach to air ventilation rate estimation from CO measurement data is presented. The approach is based on a state-space dynamic statistical model, allowing for quick and efficient estimation. Underlying computations are based on Kalman filtering, whose practical software implementation is rather easy. The key property is the flexibility of the model, allowing various artificial regimens of CO level manipulation to be treated. The model is semi-parametric in nature and can efficiently handle time-varying ventilation rate. This is a major advantage, compared to some of the methods which are currently in practical use. After a formal introduction of the statistical model, its performance is demonstrated on real data from routine measurements. It is shown how the approach can be utilized in a more complex situation of major practical relevance, when time-varying air ventilation rate and radon entry rate are to be estimated simultaneously from concurrent radon and CO measurements

Appplication of statistical mechanical methods to the modeling of social networks

Science.gov (United States)

Strathman, Anthony Robert

With the recent availability of large-scale social data sets, social networks have become open to quantitative analysis via the methods of statistical physics. We examine the statistical properties of a real large-scale social network, generated from cellular phone call-trace logs. We find this network, like many other social networks to be assortative (r = 0.31) and clustered (i.e., strongly transitive, C = 0.21). We measure fluctuation scaling to identify the presence of internal structure in the network and find that structural inhomogeneity effectively disappears at the scale of a few hundred nodes, though there is no sharp cutoff. We introduce an agent-based model of social behavior, designed to model the formation and dissolution of social ties. The model is a modified Metropolis algorithm containing agents operating under the basic sociological constraints of reciprocity, communication need and transitivity. The model introduces the concept of a social temperature. We go on to show that this simple model reproduces the global statistical network features (incl. assortativity, connected fraction, mean degree, clustering, and mean shortest path length) of the real network data and undergoes two phase transitions, one being from a "gas" to a "liquid" state and the second from a liquid to a glassy state as function of this social temperature.

Progressive statistics for studies in sports medicine and exercise science.

Science.gov (United States)

Hopkins, William G; Marshall, Stephen W; Batterham, Alan M; Hanin, Juri

2009-01-01

Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.

Terminal-Dependent Statistical Inference for the FBSDEs Models

Directory of Open Access Journals (Sweden)

Yunquan Song

2014-01-01

Full Text Available The original stochastic differential equations (OSDEs and forward-backward stochastic differential equations (FBSDEs are often used to model complex dynamic process that arise in financial, ecological, and many other areas. The main difference between OSDEs and FBSDEs is that the latter is designed to depend on a terminal condition, which is a key factor in some financial and ecological circumstances. It is interesting but challenging to estimate FBSDE parameters from noisy data and the terminal condition. However, to the best of our knowledge, the terminal-dependent statistical inference for such a model has not been explored in the existing literature. We proposed a nonparametric terminal control variables estimation method to address this problem. The reason why we use the terminal control variables is that the newly proposed inference procedures inherit the terminal-dependent characteristic. Through this new proposed method, the estimators of the functional coefficients of the FBSDEs model are obtained. The asymptotic properties of the estimators are also discussed. Simulation studies show that the proposed method gives satisfying estimates for the FBSDE parameters from noisy data and the terminal condition. A simulation is performed to test the feasibility of our method.

Statistical 3D damage accumulation model for ion implant simulators

CERN Document Server

Hernandez-Mangas, J M; Enriquez, L E; Bailon, L; Barbolla, J; Jaraiz, M

2003-01-01

A statistical 3D damage accumulation model, based on the modified Kinchin-Pease formula, for ion implant simulation has been included in our physically based ion implantation code. It has only one fitting parameter for electronic stopping and uses 3D electron density distributions for different types of targets including compound semiconductors. Also, a statistical noise reduction mechanism based on the dose division is used. The model has been adapted to be run under parallel execution in order to speed up the calculation in 3D structures. Sequential ion implantation has been modelled including previous damage profiles. It can also simulate the implantation of molecular and cluster projectiles. Comparisons of simulated doping profiles with experimental SIMS profiles are presented. Also comparisons between simulated amorphization and experimental RBS profiles are shown. An analysis of sequential versus parallel processing is provided.

Statistical 3D damage accumulation model for ion implant simulators

International Nuclear Information System (INIS)

Hernandez-Mangas, J.M.; Lazaro, J.; Enriquez, L.; Bailon, L.; Barbolla, J.; Jaraiz, M.

2003-01-01

A statistical 3D damage accumulation model, based on the modified Kinchin-Pease formula, for ion implant simulation has been included in our physically based ion implantation code. It has only one fitting parameter for electronic stopping and uses 3D electron density distributions for different types of targets including compound semiconductors. Also, a statistical noise reduction mechanism based on the dose division is used. The model has been adapted to be run under parallel execution in order to speed up the calculation in 3D structures. Sequential ion implantation has been modelled including previous damage profiles. It can also simulate the implantation of molecular and cluster projectiles. Comparisons of simulated doping profiles with experimental SIMS profiles are presented. Also comparisons between simulated amorphization and experimental RBS profiles are shown. An analysis of sequential versus parallel processing is provided

Statistical modelling of space-time processes with application to wind power

DEFF Research Database (Denmark)

Lenzi, Amanda

. This thesis aims at contributing to the wind power literature by building and evaluating new statistical techniques for producing forecasts at multiple locations and lead times using spatio-temporal information. By exploring the features of a rich portfolio of wind farms in western Denmark, we investigate...... propose spatial models for predicting wind power generation at two different time scales: for annual average wind power generation and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial...

A multi-scale and model approach to estimate future tidal high water statistics in the southern German Bright

Science.gov (United States)

Hein, H.; Mai, S.; Mayer, B.; Pohlmann, T.; Barjenbruch, U.

2012-04-01

The interactions of tides, external surges, storm surges and waves with an additional role of the coastal bathymetry define the probability of extreme water levels at the coast. Probabilistic analysis and also process based numerical models allow the estimation of future states. From the physical point of view both, deterministic processes and stochastic residuals are the fundamentals of high water statistics. This study uses a so called model chain to reproduce historic statistics of tidal high water levels (Thw) as well as the prediction of future statistics high water levels. The results of the numerical models are post-processed by a stochastic analysis. Recent studies show, that for future extrapolation of extreme Thw nonstationary parametric approaches are required. With the presented methods a better prediction of time depended parameter sets seems possible. The investigation region of this study is the southern German Bright. The model-chain is the representation of a downscaling process, which starts with an emissions scenario. Regional atmospheric and ocean models refine the results of global climate models. The concept of downscaling was chosen to resolve coastal topography sufficiently. The North Sea and estuaries are modeled with the three-dimensional model HAMburg Shelf Ocean Model. The running time includes 150 years (1950 - 2100). Results of four different hindcast runs and also of one future prediction run are validated. Based on multi-scale analysis and the theory of entropy we analyze whether any significant periodicities are represented numerically. Results show that also hindcasting the climate of Thw with a model chain for the last 60 years is a challenging task. For example, an additional modeling activity must be the inclusion of tides into regional climate ocean models. It is found that the statistics of climate variables derived from model results differs from the statistics derived from measurements. E.g. there are considerable shifts in